Parallel learning of automatic classes of languages
We introduce and explore a model for parallel learning of families of languages computable by finite automata. In this model, an algorithmic or automatic learner takes on n different input languages and identifies at least m of them correctly. For finite parallel learning, for large enough families,...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Jain, Sanjay [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2016transfer abstract |
|---|
| Schlagwörter: |
|---|
| Umfang: |
20 |
|---|
| Übergeordnetes Werk: |
Enthalten in: Influence of bulk fibre properties of PAN-based carbon felts on their performance in vanadium redox flow batteries - Schweiss, Rüdiger ELSEVIER, 2015transfer abstract, the journal of the EATCS, Amsterdam [u.a.] |
|---|---|
| Übergeordnetes Werk: |
volume:650 ; year:2016 ; day:18 ; month:10 ; pages:25-44 ; extent:20 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.tcs.2016.07.029 |
|---|
| Katalog-ID: |
ELV040057739 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | ELV040057739 | ||
| 003 | DE-627 | ||
| 005 | 20230625230719.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 180603s2016 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1016/j.tcs.2016.07.029 |2 doi | |
| 028 | 5 | 2 | |a GBVA2016010000018.pica |
| 035 | |a (DE-627)ELV040057739 | ||
| 035 | |a (ELSEVIER)S0304-3975(16)30374-7 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | |a 004 | |
| 082 | 0 | 4 | |a 004 |q DE-600 |
| 082 | 0 | 4 | |a 620 |q VZ |
| 082 | 0 | 4 | |a 690 |q VZ |
| 084 | |a 50.92 |2 bkl | ||
| 100 | 1 | |a Jain, Sanjay |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Parallel learning of automatic classes of languages |
| 264 | 1 | |c 2016transfer abstract | |
| 300 | |a 20 | ||
| 336 | |a nicht spezifiziert |b zzz |2 rdacontent | ||
| 337 | |a nicht spezifiziert |b z |2 rdamedia | ||
| 338 | |a nicht spezifiziert |b zu |2 rdacarrier | ||
| 520 | |a We introduce and explore a model for parallel learning of families of languages computable by finite automata. In this model, an algorithmic or automatic learner takes on n different input languages and identifies at least m of them correctly. For finite parallel learning, for large enough families, we establish a full characterization of learnability in terms of characteristic samples of languages. Based on this characterization, we show that it is the difference n − m , the number of languages which are potentially not identified, which is crucial. Similar results are obtained also for parallel learning in the limit. We consider also parallel finite learnability by finite automata and obtain some partial results. A number of problems for automatic variant of parallel learning remain open. | ||
| 520 | |a We introduce and explore a model for parallel learning of families of languages computable by finite automata. In this model, an algorithmic or automatic learner takes on n different input languages and identifies at least m of them correctly. For finite parallel learning, for large enough families, we establish a full characterization of learnability in terms of characteristic samples of languages. Based on this characterization, we show that it is the difference n − m , the number of languages which are potentially not identified, which is crucial. Similar results are obtained also for parallel learning in the limit. We consider also parallel finite learnability by finite automata and obtain some partial results. A number of problems for automatic variant of parallel learning remain open. | ||
| 650 | 7 | |a Inductive inference |2 Elsevier | |
| 650 | 7 | |a Automatic classes |2 Elsevier | |
| 650 | 7 | |a Parallel learning |2 Elsevier | |
| 700 | 1 | |a Kinber, Efim |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |n Elsevier |a Schweiss, Rüdiger ELSEVIER |t Influence of bulk fibre properties of PAN-based carbon felts on their performance in vanadium redox flow batteries |d 2015transfer abstract |d the journal of the EATCS |g Amsterdam [u.a.] |w (DE-627)ELV013125583 |
| 773 | 1 | 8 | |g volume:650 |g year:2016 |g day:18 |g month:10 |g pages:25-44 |g extent:20 |
| 856 | 4 | 0 | |u https://doi.org/10.1016/j.tcs.2016.07.029 |3 Volltext |
| 912 | |a GBV_USEFLAG_U | ||
| 912 | |a GBV_ELV | ||
| 912 | |a SYSFLAG_U | ||
| 912 | |a GBV_ILN_22 | ||
| 912 | |a GBV_ILN_40 | ||
| 936 | b | k | |a 50.92 |j Meerestechnik |q VZ |
| 951 | |a AR | ||
| 952 | |d 650 |j 2016 |b 18 |c 1018 |h 25-44 |g 20 | ||
| 953 | |2 045F |a 004 | ||
| author_variant |
s j sj |
|---|---|
| matchkey_str |
jainsanjaykinberefim:2016----:aallerigfuoaicas |
| hierarchy_sort_str |
2016transfer abstract |
| bklnumber |
50.92 |
| publishDate |
2016 |
| allfields |
10.1016/j.tcs.2016.07.029 doi GBVA2016010000018.pica (DE-627)ELV040057739 (ELSEVIER)S0304-3975(16)30374-7 DE-627 ger DE-627 rakwb eng 004 004 DE-600 620 VZ 690 VZ 50.92 bkl Jain, Sanjay verfasserin aut Parallel learning of automatic classes of languages 2016transfer abstract 20 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier We introduce and explore a model for parallel learning of families of languages computable by finite automata. In this model, an algorithmic or automatic learner takes on n different input languages and identifies at least m of them correctly. For finite parallel learning, for large enough families, we establish a full characterization of learnability in terms of characteristic samples of languages. Based on this characterization, we show that it is the difference n − m , the number of languages which are potentially not identified, which is crucial. Similar results are obtained also for parallel learning in the limit. We consider also parallel finite learnability by finite automata and obtain some partial results. A number of problems for automatic variant of parallel learning remain open. We introduce and explore a model for parallel learning of families of languages computable by finite automata. In this model, an algorithmic or automatic learner takes on n different input languages and identifies at least m of them correctly. For finite parallel learning, for large enough families, we establish a full characterization of learnability in terms of characteristic samples of languages. Based on this characterization, we show that it is the difference n − m , the number of languages which are potentially not identified, which is crucial. Similar results are obtained also for parallel learning in the limit. We consider also parallel finite learnability by finite automata and obtain some partial results. A number of problems for automatic variant of parallel learning remain open. Inductive inference Elsevier Automatic classes Elsevier Parallel learning Elsevier Kinber, Efim oth Enthalten in Elsevier Schweiss, Rüdiger ELSEVIER Influence of bulk fibre properties of PAN-based carbon felts on their performance in vanadium redox flow batteries 2015transfer abstract the journal of the EATCS Amsterdam [u.a.] (DE-627)ELV013125583 volume:650 year:2016 day:18 month:10 pages:25-44 extent:20 https://doi.org/10.1016/j.tcs.2016.07.029 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_22 GBV_ILN_40 50.92 Meerestechnik VZ AR 650 2016 18 1018 25-44 20 045F 004 |
| spelling |
10.1016/j.tcs.2016.07.029 doi GBVA2016010000018.pica (DE-627)ELV040057739 (ELSEVIER)S0304-3975(16)30374-7 DE-627 ger DE-627 rakwb eng 004 004 DE-600 620 VZ 690 VZ 50.92 bkl Jain, Sanjay verfasserin aut Parallel learning of automatic classes of languages 2016transfer abstract 20 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier We introduce and explore a model for parallel learning of families of languages computable by finite automata. In this model, an algorithmic or automatic learner takes on n different input languages and identifies at least m of them correctly. For finite parallel learning, for large enough families, we establish a full characterization of learnability in terms of characteristic samples of languages. Based on this characterization, we show that it is the difference n − m , the number of languages which are potentially not identified, which is crucial. Similar results are obtained also for parallel learning in the limit. We consider also parallel finite learnability by finite automata and obtain some partial results. A number of problems for automatic variant of parallel learning remain open. We introduce and explore a model for parallel learning of families of languages computable by finite automata. In this model, an algorithmic or automatic learner takes on n different input languages and identifies at least m of them correctly. For finite parallel learning, for large enough families, we establish a full characterization of learnability in terms of characteristic samples of languages. Based on this characterization, we show that it is the difference n − m , the number of languages which are potentially not identified, which is crucial. Similar results are obtained also for parallel learning in the limit. We consider also parallel finite learnability by finite automata and obtain some partial results. A number of problems for automatic variant of parallel learning remain open. Inductive inference Elsevier Automatic classes Elsevier Parallel learning Elsevier Kinber, Efim oth Enthalten in Elsevier Schweiss, Rüdiger ELSEVIER Influence of bulk fibre properties of PAN-based carbon felts on their performance in vanadium redox flow batteries 2015transfer abstract the journal of the EATCS Amsterdam [u.a.] (DE-627)ELV013125583 volume:650 year:2016 day:18 month:10 pages:25-44 extent:20 https://doi.org/10.1016/j.tcs.2016.07.029 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_22 GBV_ILN_40 50.92 Meerestechnik VZ AR 650 2016 18 1018 25-44 20 045F 004 |
| allfields_unstemmed |
10.1016/j.tcs.2016.07.029 doi GBVA2016010000018.pica (DE-627)ELV040057739 (ELSEVIER)S0304-3975(16)30374-7 DE-627 ger DE-627 rakwb eng 004 004 DE-600 620 VZ 690 VZ 50.92 bkl Jain, Sanjay verfasserin aut Parallel learning of automatic classes of languages 2016transfer abstract 20 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier We introduce and explore a model for parallel learning of families of languages computable by finite automata. In this model, an algorithmic or automatic learner takes on n different input languages and identifies at least m of them correctly. For finite parallel learning, for large enough families, we establish a full characterization of learnability in terms of characteristic samples of languages. Based on this characterization, we show that it is the difference n − m , the number of languages which are potentially not identified, which is crucial. Similar results are obtained also for parallel learning in the limit. We consider also parallel finite learnability by finite automata and obtain some partial results. A number of problems for automatic variant of parallel learning remain open. We introduce and explore a model for parallel learning of families of languages computable by finite automata. In this model, an algorithmic or automatic learner takes on n different input languages and identifies at least m of them correctly. For finite parallel learning, for large enough families, we establish a full characterization of learnability in terms of characteristic samples of languages. Based on this characterization, we show that it is the difference n − m , the number of languages which are potentially not identified, which is crucial. Similar results are obtained also for parallel learning in the limit. We consider also parallel finite learnability by finite automata and obtain some partial results. A number of problems for automatic variant of parallel learning remain open. Inductive inference Elsevier Automatic classes Elsevier Parallel learning Elsevier Kinber, Efim oth Enthalten in Elsevier Schweiss, Rüdiger ELSEVIER Influence of bulk fibre properties of PAN-based carbon felts on their performance in vanadium redox flow batteries 2015transfer abstract the journal of the EATCS Amsterdam [u.a.] (DE-627)ELV013125583 volume:650 year:2016 day:18 month:10 pages:25-44 extent:20 https://doi.org/10.1016/j.tcs.2016.07.029 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_22 GBV_ILN_40 50.92 Meerestechnik VZ AR 650 2016 18 1018 25-44 20 045F 004 |
| allfieldsGer |
10.1016/j.tcs.2016.07.029 doi GBVA2016010000018.pica (DE-627)ELV040057739 (ELSEVIER)S0304-3975(16)30374-7 DE-627 ger DE-627 rakwb eng 004 004 DE-600 620 VZ 690 VZ 50.92 bkl Jain, Sanjay verfasserin aut Parallel learning of automatic classes of languages 2016transfer abstract 20 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier We introduce and explore a model for parallel learning of families of languages computable by finite automata. In this model, an algorithmic or automatic learner takes on n different input languages and identifies at least m of them correctly. For finite parallel learning, for large enough families, we establish a full characterization of learnability in terms of characteristic samples of languages. Based on this characterization, we show that it is the difference n − m , the number of languages which are potentially not identified, which is crucial. Similar results are obtained also for parallel learning in the limit. We consider also parallel finite learnability by finite automata and obtain some partial results. A number of problems for automatic variant of parallel learning remain open. We introduce and explore a model for parallel learning of families of languages computable by finite automata. In this model, an algorithmic or automatic learner takes on n different input languages and identifies at least m of them correctly. For finite parallel learning, for large enough families, we establish a full characterization of learnability in terms of characteristic samples of languages. Based on this characterization, we show that it is the difference n − m , the number of languages which are potentially not identified, which is crucial. Similar results are obtained also for parallel learning in the limit. We consider also parallel finite learnability by finite automata and obtain some partial results. A number of problems for automatic variant of parallel learning remain open. Inductive inference Elsevier Automatic classes Elsevier Parallel learning Elsevier Kinber, Efim oth Enthalten in Elsevier Schweiss, Rüdiger ELSEVIER Influence of bulk fibre properties of PAN-based carbon felts on their performance in vanadium redox flow batteries 2015transfer abstract the journal of the EATCS Amsterdam [u.a.] (DE-627)ELV013125583 volume:650 year:2016 day:18 month:10 pages:25-44 extent:20 https://doi.org/10.1016/j.tcs.2016.07.029 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_22 GBV_ILN_40 50.92 Meerestechnik VZ AR 650 2016 18 1018 25-44 20 045F 004 |
| allfieldsSound |
10.1016/j.tcs.2016.07.029 doi GBVA2016010000018.pica (DE-627)ELV040057739 (ELSEVIER)S0304-3975(16)30374-7 DE-627 ger DE-627 rakwb eng 004 004 DE-600 620 VZ 690 VZ 50.92 bkl Jain, Sanjay verfasserin aut Parallel learning of automatic classes of languages 2016transfer abstract 20 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier We introduce and explore a model for parallel learning of families of languages computable by finite automata. In this model, an algorithmic or automatic learner takes on n different input languages and identifies at least m of them correctly. For finite parallel learning, for large enough families, we establish a full characterization of learnability in terms of characteristic samples of languages. Based on this characterization, we show that it is the difference n − m , the number of languages which are potentially not identified, which is crucial. Similar results are obtained also for parallel learning in the limit. We consider also parallel finite learnability by finite automata and obtain some partial results. A number of problems for automatic variant of parallel learning remain open. We introduce and explore a model for parallel learning of families of languages computable by finite automata. In this model, an algorithmic or automatic learner takes on n different input languages and identifies at least m of them correctly. For finite parallel learning, for large enough families, we establish a full characterization of learnability in terms of characteristic samples of languages. Based on this characterization, we show that it is the difference n − m , the number of languages which are potentially not identified, which is crucial. Similar results are obtained also for parallel learning in the limit. We consider also parallel finite learnability by finite automata and obtain some partial results. A number of problems for automatic variant of parallel learning remain open. Inductive inference Elsevier Automatic classes Elsevier Parallel learning Elsevier Kinber, Efim oth Enthalten in Elsevier Schweiss, Rüdiger ELSEVIER Influence of bulk fibre properties of PAN-based carbon felts on their performance in vanadium redox flow batteries 2015transfer abstract the journal of the EATCS Amsterdam [u.a.] (DE-627)ELV013125583 volume:650 year:2016 day:18 month:10 pages:25-44 extent:20 https://doi.org/10.1016/j.tcs.2016.07.029 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_22 GBV_ILN_40 50.92 Meerestechnik VZ AR 650 2016 18 1018 25-44 20 045F 004 |
| language |
English |
| source |
Enthalten in Influence of bulk fibre properties of PAN-based carbon felts on their performance in vanadium redox flow batteries Amsterdam [u.a.] volume:650 year:2016 day:18 month:10 pages:25-44 extent:20 |
| sourceStr |
Enthalten in Influence of bulk fibre properties of PAN-based carbon felts on their performance in vanadium redox flow batteries Amsterdam [u.a.] volume:650 year:2016 day:18 month:10 pages:25-44 extent:20 |
| format_phy_str_mv |
Article |
| bklname |
Meerestechnik |
| institution |
findex.gbv.de |
| topic_facet |
Inductive inference Automatic classes Parallel learning |
| dewey-raw |
004 |
| isfreeaccess_bool |
false |
| container_title |
Influence of bulk fibre properties of PAN-based carbon felts on their performance in vanadium redox flow batteries |
| authorswithroles_txt_mv |
Jain, Sanjay @@aut@@ Kinber, Efim @@oth@@ |
| publishDateDaySort_date |
2016-01-18T00:00:00Z |
| hierarchy_top_id |
ELV013125583 |
| dewey-sort |
14 |
| id |
ELV040057739 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV040057739</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230625230719.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">180603s2016 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.tcs.2016.07.029</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">GBVA2016010000018.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV040057739</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0304-3975(16)30374-7</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">004</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">DE-600</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">620</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">690</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">50.92</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Jain, Sanjay</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Parallel learning of automatic classes of languages</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2016transfer abstract</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">20</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">We introduce and explore a model for parallel learning of families of languages computable by finite automata. In this model, an algorithmic or automatic learner takes on n different input languages and identifies at least m of them correctly. For finite parallel learning, for large enough families, we establish a full characterization of learnability in terms of characteristic samples of languages. Based on this characterization, we show that it is the difference n − m , the number of languages which are potentially not identified, which is crucial. Similar results are obtained also for parallel learning in the limit. We consider also parallel finite learnability by finite automata and obtain some partial results. A number of problems for automatic variant of parallel learning remain open.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">We introduce and explore a model for parallel learning of families of languages computable by finite automata. In this model, an algorithmic or automatic learner takes on n different input languages and identifies at least m of them correctly. For finite parallel learning, for large enough families, we establish a full characterization of learnability in terms of characteristic samples of languages. Based on this characterization, we show that it is the difference n − m , the number of languages which are potentially not identified, which is crucial. Similar results are obtained also for parallel learning in the limit. We consider also parallel finite learnability by finite automata and obtain some partial results. A number of problems for automatic variant of parallel learning remain open.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Inductive inference</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Automatic classes</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Parallel learning</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Kinber, Efim</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier</subfield><subfield code="a">Schweiss, Rüdiger ELSEVIER</subfield><subfield code="t">Influence of bulk fibre properties of PAN-based carbon felts on their performance in vanadium redox flow batteries</subfield><subfield code="d">2015transfer abstract</subfield><subfield code="d">the journal of the EATCS</subfield><subfield code="g">Amsterdam [u.a.]</subfield><subfield code="w">(DE-627)ELV013125583</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:650</subfield><subfield code="g">year:2016</subfield><subfield code="g">day:18</subfield><subfield code="g">month:10</subfield><subfield code="g">pages:25-44</subfield><subfield code="g">extent:20</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.tcs.2016.07.029</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">50.92</subfield><subfield code="j">Meerestechnik</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">650</subfield><subfield code="j">2016</subfield><subfield code="b">18</subfield><subfield code="c">1018</subfield><subfield code="h">25-44</subfield><subfield code="g">20</subfield></datafield><datafield tag="953" ind1=" " ind2=" "><subfield code="2">045F</subfield><subfield code="a">004</subfield></datafield></record></collection>
|
| author |
Jain, Sanjay |
| spellingShingle |
Jain, Sanjay ddc 004 ddc 620 ddc 690 bkl 50.92 Elsevier Inductive inference Elsevier Automatic classes Elsevier Parallel learning Parallel learning of automatic classes of languages |
| authorStr |
Jain, Sanjay |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)ELV013125583 |
| format |
electronic Article |
| dewey-ones |
004 - Data processing & computer science 620 - Engineering & allied operations 690 - Buildings |
| delete_txt_mv |
keep |
| author_role |
aut |
| collection |
elsevier |
| remote_str |
true |
| illustrated |
Not Illustrated |
| topic_title |
004 004 DE-600 620 VZ 690 VZ 50.92 bkl Parallel learning of automatic classes of languages Inductive inference Elsevier Automatic classes Elsevier Parallel learning Elsevier |
| topic |
ddc 004 ddc 620 ddc 690 bkl 50.92 Elsevier Inductive inference Elsevier Automatic classes Elsevier Parallel learning |
| topic_unstemmed |
ddc 004 ddc 620 ddc 690 bkl 50.92 Elsevier Inductive inference Elsevier Automatic classes Elsevier Parallel learning |
| topic_browse |
ddc 004 ddc 620 ddc 690 bkl 50.92 Elsevier Inductive inference Elsevier Automatic classes Elsevier Parallel learning |
| format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
zu |
| author2_variant |
e k ek |
| hierarchy_parent_title |
Influence of bulk fibre properties of PAN-based carbon felts on their performance in vanadium redox flow batteries |
| hierarchy_parent_id |
ELV013125583 |
| dewey-tens |
000 - Computer science, knowledge & systems 620 - Engineering 690 - Building & construction |
| hierarchy_top_title |
Influence of bulk fibre properties of PAN-based carbon felts on their performance in vanadium redox flow batteries |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)ELV013125583 |
| title |
Parallel learning of automatic classes of languages |
| ctrlnum |
(DE-627)ELV040057739 (ELSEVIER)S0304-3975(16)30374-7 |
| title_full |
Parallel learning of automatic classes of languages |
| author_sort |
Jain, Sanjay |
| journal |
Influence of bulk fibre properties of PAN-based carbon felts on their performance in vanadium redox flow batteries |
| journalStr |
Influence of bulk fibre properties of PAN-based carbon felts on their performance in vanadium redox flow batteries |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
000 - Computer science, information & general works 600 - Technology |
| recordtype |
marc |
| publishDateSort |
2016 |
| contenttype_str_mv |
zzz |
| container_start_page |
25 |
| author_browse |
Jain, Sanjay |
| container_volume |
650 |
| physical |
20 |
| class |
004 004 DE-600 620 VZ 690 VZ 50.92 bkl |
| format_se |
Elektronische Aufsätze |
| author-letter |
Jain, Sanjay |
| doi_str_mv |
10.1016/j.tcs.2016.07.029 |
| dewey-full |
004 620 690 |
| title_sort |
parallel learning of automatic classes of languages |
| title_auth |
Parallel learning of automatic classes of languages |
| abstract |
We introduce and explore a model for parallel learning of families of languages computable by finite automata. In this model, an algorithmic or automatic learner takes on n different input languages and identifies at least m of them correctly. For finite parallel learning, for large enough families, we establish a full characterization of learnability in terms of characteristic samples of languages. Based on this characterization, we show that it is the difference n − m , the number of languages which are potentially not identified, which is crucial. Similar results are obtained also for parallel learning in the limit. We consider also parallel finite learnability by finite automata and obtain some partial results. A number of problems for automatic variant of parallel learning remain open. |
| abstractGer |
We introduce and explore a model for parallel learning of families of languages computable by finite automata. In this model, an algorithmic or automatic learner takes on n different input languages and identifies at least m of them correctly. For finite parallel learning, for large enough families, we establish a full characterization of learnability in terms of characteristic samples of languages. Based on this characterization, we show that it is the difference n − m , the number of languages which are potentially not identified, which is crucial. Similar results are obtained also for parallel learning in the limit. We consider also parallel finite learnability by finite automata and obtain some partial results. A number of problems for automatic variant of parallel learning remain open. |
| abstract_unstemmed |
We introduce and explore a model for parallel learning of families of languages computable by finite automata. In this model, an algorithmic or automatic learner takes on n different input languages and identifies at least m of them correctly. For finite parallel learning, for large enough families, we establish a full characterization of learnability in terms of characteristic samples of languages. Based on this characterization, we show that it is the difference n − m , the number of languages which are potentially not identified, which is crucial. Similar results are obtained also for parallel learning in the limit. We consider also parallel finite learnability by finite automata and obtain some partial results. A number of problems for automatic variant of parallel learning remain open. |
| collection_details |
GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_22 GBV_ILN_40 |
| title_short |
Parallel learning of automatic classes of languages |
| url |
https://doi.org/10.1016/j.tcs.2016.07.029 |
| remote_bool |
true |
| author2 |
Kinber, Efim |
| author2Str |
Kinber, Efim |
| ppnlink |
ELV013125583 |
| mediatype_str_mv |
z |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| author2_role |
oth |
| doi_str |
10.1016/j.tcs.2016.07.029 |
| up_date |
2024-07-06T16:32:56.022Z |
| _version_ |
1803848071013990400 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV040057739</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230625230719.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">180603s2016 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.tcs.2016.07.029</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">GBVA2016010000018.pica</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV040057739</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0304-3975(16)30374-7</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">004</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">DE-600</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">620</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">690</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">50.92</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Jain, Sanjay</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Parallel learning of automatic classes of languages</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2016transfer abstract</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">20</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">z</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zu</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">We introduce and explore a model for parallel learning of families of languages computable by finite automata. In this model, an algorithmic or automatic learner takes on n different input languages and identifies at least m of them correctly. For finite parallel learning, for large enough families, we establish a full characterization of learnability in terms of characteristic samples of languages. Based on this characterization, we show that it is the difference n − m , the number of languages which are potentially not identified, which is crucial. Similar results are obtained also for parallel learning in the limit. We consider also parallel finite learnability by finite automata and obtain some partial results. A number of problems for automatic variant of parallel learning remain open.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">We introduce and explore a model for parallel learning of families of languages computable by finite automata. In this model, an algorithmic or automatic learner takes on n different input languages and identifies at least m of them correctly. For finite parallel learning, for large enough families, we establish a full characterization of learnability in terms of characteristic samples of languages. Based on this characterization, we show that it is the difference n − m , the number of languages which are potentially not identified, which is crucial. Similar results are obtained also for parallel learning in the limit. We consider also parallel finite learnability by finite automata and obtain some partial results. A number of problems for automatic variant of parallel learning remain open.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Inductive inference</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Automatic classes</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Parallel learning</subfield><subfield code="2">Elsevier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Kinber, Efim</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="n">Elsevier</subfield><subfield code="a">Schweiss, Rüdiger ELSEVIER</subfield><subfield code="t">Influence of bulk fibre properties of PAN-based carbon felts on their performance in vanadium redox flow batteries</subfield><subfield code="d">2015transfer abstract</subfield><subfield code="d">the journal of the EATCS</subfield><subfield code="g">Amsterdam [u.a.]</subfield><subfield code="w">(DE-627)ELV013125583</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:650</subfield><subfield code="g">year:2016</subfield><subfield code="g">day:18</subfield><subfield code="g">month:10</subfield><subfield code="g">pages:25-44</subfield><subfield code="g">extent:20</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1016/j.tcs.2016.07.029</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">50.92</subfield><subfield code="j">Meerestechnik</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">650</subfield><subfield code="j">2016</subfield><subfield code="b">18</subfield><subfield code="c">1018</subfield><subfield code="h">25-44</subfield><subfield code="g">20</subfield></datafield><datafield tag="953" ind1=" " ind2=" "><subfield code="2">045F</subfield><subfield code="a">004</subfield></datafield></record></collection>
|
| score |
7.398242 |