Automatic learning from positive data and negative counterexamples
We introduce and study a model for learning in the limit by finite automata from positive data and negative counterexamples. The focus is on learning classes of languages with the membership problem computable by finite automata (so-called automatic classes). We show that, within the framework of ou...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Jain, Sanjay [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2017transfer abstract |
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| Schlagwörter: |
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| Umfang: |
23 |
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| Übergeordnetes Werk: |
Enthalten in: Complex prosthetic joint infections due to carbapenemase-producing Klebsiella pneumoniae: a unique challenge in the era of untreatable infections - de Sanctis, Jorgelina ELSEVIER, 2014, Amsterdam |
|---|---|
| Übergeordnetes Werk: |
volume:255 ; year:2017 ; pages:45-67 ; extent:23 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.ic.2017.05.002 |
|---|
| Katalog-ID: |
ELV035988371 |
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| 520 | |a We introduce and study a model for learning in the limit by finite automata from positive data and negative counterexamples. The focus is on learning classes of languages with the membership problem computable by finite automata (so-called automatic classes). We show that, within the framework of our model, finite automata (automatic learners) can learn all automatic classes when memory of a learner is restricted by the size of the longest datum seen so far. We also study capabilities of automatic learners in our model with other restrictions on the memory and how the choice of negative counterexamples (arbitrary, or least, or the ones which are bounded by the largest positive datum seen so far) can impact automatic learnability. | ||
| 520 | |a We introduce and study a model for learning in the limit by finite automata from positive data and negative counterexamples. The focus is on learning classes of languages with the membership problem computable by finite automata (so-called automatic classes). We show that, within the framework of our model, finite automata (automatic learners) can learn all automatic classes when memory of a learner is restricted by the size of the longest datum seen so far. We also study capabilities of automatic learners in our model with other restrictions on the memory and how the choice of negative counterexamples (arbitrary, or least, or the ones which are bounded by the largest positive datum seen so far) can impact automatic learnability. | ||
| 650 | 7 | |a Inductive inference |2 Elsevier | |
| 650 | 7 | |a Automatic learning |2 Elsevier | |
| 650 | 7 | |a Automatic classes |2 Elsevier | |
| 650 | 7 | |a Negative counterexamples |2 Elsevier | |
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| 700 | 1 | |a Stephan, Frank |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |n Elsevier |a de Sanctis, Jorgelina ELSEVIER |t Complex prosthetic joint infections due to carbapenemase-producing Klebsiella pneumoniae: a unique challenge in the era of untreatable infections |d 2014 |g Amsterdam |w (DE-627)ELV017851467 |
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10.1016/j.ic.2017.05.002 doi GBVA2017014000021.pica (DE-627)ELV035988371 (ELSEVIER)S0890-5401(17)30092-5 DE-627 ger DE-627 rakwb eng 330 004 330 DE-600 004 DE-600 610 VZ 690 610 600 VZ 30.00 bkl 44.13 bkl Jain, Sanjay verfasserin aut Automatic learning from positive data and negative counterexamples 2017transfer abstract 23 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier We introduce and study a model for learning in the limit by finite automata from positive data and negative counterexamples. The focus is on learning classes of languages with the membership problem computable by finite automata (so-called automatic classes). We show that, within the framework of our model, finite automata (automatic learners) can learn all automatic classes when memory of a learner is restricted by the size of the longest datum seen so far. We also study capabilities of automatic learners in our model with other restrictions on the memory and how the choice of negative counterexamples (arbitrary, or least, or the ones which are bounded by the largest positive datum seen so far) can impact automatic learnability. We introduce and study a model for learning in the limit by finite automata from positive data and negative counterexamples. The focus is on learning classes of languages with the membership problem computable by finite automata (so-called automatic classes). We show that, within the framework of our model, finite automata (automatic learners) can learn all automatic classes when memory of a learner is restricted by the size of the longest datum seen so far. We also study capabilities of automatic learners in our model with other restrictions on the memory and how the choice of negative counterexamples (arbitrary, or least, or the ones which are bounded by the largest positive datum seen so far) can impact automatic learnability. Inductive inference Elsevier Automatic learning Elsevier Automatic classes Elsevier Negative counterexamples Elsevier Iterative learning Elsevier Kinber, Efim oth Stephan, Frank oth Enthalten in Elsevier de Sanctis, Jorgelina ELSEVIER Complex prosthetic joint infections due to carbapenemase-producing Klebsiella pneumoniae: a unique challenge in the era of untreatable infections 2014 Amsterdam (DE-627)ELV017851467 volume:255 year:2017 pages:45-67 extent:23 https://doi.org/10.1016/j.ic.2017.05.002 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_65 GBV_ILN_70 30.00 Naturwissenschaften allgemein: Allgemeines VZ 44.13 Medizinische Ökologie VZ AR 255 2017 45-67 23 045F 330 |
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10.1016/j.ic.2017.05.002 doi GBVA2017014000021.pica (DE-627)ELV035988371 (ELSEVIER)S0890-5401(17)30092-5 DE-627 ger DE-627 rakwb eng 330 004 330 DE-600 004 DE-600 610 VZ 690 610 600 VZ 30.00 bkl 44.13 bkl Jain, Sanjay verfasserin aut Automatic learning from positive data and negative counterexamples 2017transfer abstract 23 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier We introduce and study a model for learning in the limit by finite automata from positive data and negative counterexamples. The focus is on learning classes of languages with the membership problem computable by finite automata (so-called automatic classes). We show that, within the framework of our model, finite automata (automatic learners) can learn all automatic classes when memory of a learner is restricted by the size of the longest datum seen so far. We also study capabilities of automatic learners in our model with other restrictions on the memory and how the choice of negative counterexamples (arbitrary, or least, or the ones which are bounded by the largest positive datum seen so far) can impact automatic learnability. We introduce and study a model for learning in the limit by finite automata from positive data and negative counterexamples. The focus is on learning classes of languages with the membership problem computable by finite automata (so-called automatic classes). We show that, within the framework of our model, finite automata (automatic learners) can learn all automatic classes when memory of a learner is restricted by the size of the longest datum seen so far. We also study capabilities of automatic learners in our model with other restrictions on the memory and how the choice of negative counterexamples (arbitrary, or least, or the ones which are bounded by the largest positive datum seen so far) can impact automatic learnability. Inductive inference Elsevier Automatic learning Elsevier Automatic classes Elsevier Negative counterexamples Elsevier Iterative learning Elsevier Kinber, Efim oth Stephan, Frank oth Enthalten in Elsevier de Sanctis, Jorgelina ELSEVIER Complex prosthetic joint infections due to carbapenemase-producing Klebsiella pneumoniae: a unique challenge in the era of untreatable infections 2014 Amsterdam (DE-627)ELV017851467 volume:255 year:2017 pages:45-67 extent:23 https://doi.org/10.1016/j.ic.2017.05.002 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_65 GBV_ILN_70 30.00 Naturwissenschaften allgemein: Allgemeines VZ 44.13 Medizinische Ökologie VZ AR 255 2017 45-67 23 045F 330 |
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10.1016/j.ic.2017.05.002 doi GBVA2017014000021.pica (DE-627)ELV035988371 (ELSEVIER)S0890-5401(17)30092-5 DE-627 ger DE-627 rakwb eng 330 004 330 DE-600 004 DE-600 610 VZ 690 610 600 VZ 30.00 bkl 44.13 bkl Jain, Sanjay verfasserin aut Automatic learning from positive data and negative counterexamples 2017transfer abstract 23 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier We introduce and study a model for learning in the limit by finite automata from positive data and negative counterexamples. The focus is on learning classes of languages with the membership problem computable by finite automata (so-called automatic classes). We show that, within the framework of our model, finite automata (automatic learners) can learn all automatic classes when memory of a learner is restricted by the size of the longest datum seen so far. We also study capabilities of automatic learners in our model with other restrictions on the memory and how the choice of negative counterexamples (arbitrary, or least, or the ones which are bounded by the largest positive datum seen so far) can impact automatic learnability. We introduce and study a model for learning in the limit by finite automata from positive data and negative counterexamples. The focus is on learning classes of languages with the membership problem computable by finite automata (so-called automatic classes). We show that, within the framework of our model, finite automata (automatic learners) can learn all automatic classes when memory of a learner is restricted by the size of the longest datum seen so far. We also study capabilities of automatic learners in our model with other restrictions on the memory and how the choice of negative counterexamples (arbitrary, or least, or the ones which are bounded by the largest positive datum seen so far) can impact automatic learnability. Inductive inference Elsevier Automatic learning Elsevier Automatic classes Elsevier Negative counterexamples Elsevier Iterative learning Elsevier Kinber, Efim oth Stephan, Frank oth Enthalten in Elsevier de Sanctis, Jorgelina ELSEVIER Complex prosthetic joint infections due to carbapenemase-producing Klebsiella pneumoniae: a unique challenge in the era of untreatable infections 2014 Amsterdam (DE-627)ELV017851467 volume:255 year:2017 pages:45-67 extent:23 https://doi.org/10.1016/j.ic.2017.05.002 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_65 GBV_ILN_70 30.00 Naturwissenschaften allgemein: Allgemeines VZ 44.13 Medizinische Ökologie VZ AR 255 2017 45-67 23 045F 330 |
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10.1016/j.ic.2017.05.002 doi GBVA2017014000021.pica (DE-627)ELV035988371 (ELSEVIER)S0890-5401(17)30092-5 DE-627 ger DE-627 rakwb eng 330 004 330 DE-600 004 DE-600 610 VZ 690 610 600 VZ 30.00 bkl 44.13 bkl Jain, Sanjay verfasserin aut Automatic learning from positive data and negative counterexamples 2017transfer abstract 23 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier We introduce and study a model for learning in the limit by finite automata from positive data and negative counterexamples. The focus is on learning classes of languages with the membership problem computable by finite automata (so-called automatic classes). We show that, within the framework of our model, finite automata (automatic learners) can learn all automatic classes when memory of a learner is restricted by the size of the longest datum seen so far. We also study capabilities of automatic learners in our model with other restrictions on the memory and how the choice of negative counterexamples (arbitrary, or least, or the ones which are bounded by the largest positive datum seen so far) can impact automatic learnability. We introduce and study a model for learning in the limit by finite automata from positive data and negative counterexamples. The focus is on learning classes of languages with the membership problem computable by finite automata (so-called automatic classes). We show that, within the framework of our model, finite automata (automatic learners) can learn all automatic classes when memory of a learner is restricted by the size of the longest datum seen so far. We also study capabilities of automatic learners in our model with other restrictions on the memory and how the choice of negative counterexamples (arbitrary, or least, or the ones which are bounded by the largest positive datum seen so far) can impact automatic learnability. Inductive inference Elsevier Automatic learning Elsevier Automatic classes Elsevier Negative counterexamples Elsevier Iterative learning Elsevier Kinber, Efim oth Stephan, Frank oth Enthalten in Elsevier de Sanctis, Jorgelina ELSEVIER Complex prosthetic joint infections due to carbapenemase-producing Klebsiella pneumoniae: a unique challenge in the era of untreatable infections 2014 Amsterdam (DE-627)ELV017851467 volume:255 year:2017 pages:45-67 extent:23 https://doi.org/10.1016/j.ic.2017.05.002 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_65 GBV_ILN_70 30.00 Naturwissenschaften allgemein: Allgemeines VZ 44.13 Medizinische Ökologie VZ AR 255 2017 45-67 23 045F 330 |
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Enthalten in Complex prosthetic joint infections due to carbapenemase-producing Klebsiella pneumoniae: a unique challenge in the era of untreatable infections Amsterdam volume:255 year:2017 pages:45-67 extent:23 |
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Enthalten in Complex prosthetic joint infections due to carbapenemase-producing Klebsiella pneumoniae: a unique challenge in the era of untreatable infections Amsterdam volume:255 year:2017 pages:45-67 extent:23 |
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Complex prosthetic joint infections due to carbapenemase-producing Klebsiella pneumoniae: a unique challenge in the era of untreatable infections |
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330 004 330 DE-600 004 DE-600 610 VZ 690 610 600 VZ 30.00 bkl 44.13 bkl Automatic learning from positive data and negative counterexamples Inductive inference Elsevier Automatic learning Elsevier Automatic classes Elsevier Negative counterexamples Elsevier Iterative learning Elsevier |
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ddc 330 ddc 004 ddc 610 ddc 690 bkl 30.00 bkl 44.13 Elsevier Inductive inference Elsevier Automatic learning Elsevier Automatic classes Elsevier Negative counterexamples Elsevier Iterative learning |
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ddc 330 ddc 004 ddc 610 ddc 690 bkl 30.00 bkl 44.13 Elsevier Inductive inference Elsevier Automatic learning Elsevier Automatic classes Elsevier Negative counterexamples Elsevier Iterative learning |
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ddc 330 ddc 004 ddc 610 ddc 690 bkl 30.00 bkl 44.13 Elsevier Inductive inference Elsevier Automatic learning Elsevier Automatic classes Elsevier Negative counterexamples Elsevier Iterative learning |
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Complex prosthetic joint infections due to carbapenemase-producing Klebsiella pneumoniae: a unique challenge in the era of untreatable infections |
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ELV017851467 |
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330 - Economics 000 - Computer science, knowledge & systems 610 - Medicine & health 690 - Building & construction 600 - Technology |
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Complex prosthetic joint infections due to carbapenemase-producing Klebsiella pneumoniae: a unique challenge in the era of untreatable infections |
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Automatic learning from positive data and negative counterexamples |
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(DE-627)ELV035988371 (ELSEVIER)S0890-5401(17)30092-5 |
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Automatic learning from positive data and negative counterexamples |
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Jain, Sanjay |
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Complex prosthetic joint infections due to carbapenemase-producing Klebsiella pneumoniae: a unique challenge in the era of untreatable infections |
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Complex prosthetic joint infections due to carbapenemase-producing Klebsiella pneumoniae: a unique challenge in the era of untreatable infections |
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Jain, Sanjay |
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330 004 330 DE-600 004 DE-600 610 VZ 690 610 600 VZ 30.00 bkl 44.13 bkl |
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Jain, Sanjay |
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10.1016/j.ic.2017.05.002 |
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330 004 610 690 600 |
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automatic learning from positive data and negative counterexamples |
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Automatic learning from positive data and negative counterexamples |
| abstract |
We introduce and study a model for learning in the limit by finite automata from positive data and negative counterexamples. The focus is on learning classes of languages with the membership problem computable by finite automata (so-called automatic classes). We show that, within the framework of our model, finite automata (automatic learners) can learn all automatic classes when memory of a learner is restricted by the size of the longest datum seen so far. We also study capabilities of automatic learners in our model with other restrictions on the memory and how the choice of negative counterexamples (arbitrary, or least, or the ones which are bounded by the largest positive datum seen so far) can impact automatic learnability. |
| abstractGer |
We introduce and study a model for learning in the limit by finite automata from positive data and negative counterexamples. The focus is on learning classes of languages with the membership problem computable by finite automata (so-called automatic classes). We show that, within the framework of our model, finite automata (automatic learners) can learn all automatic classes when memory of a learner is restricted by the size of the longest datum seen so far. We also study capabilities of automatic learners in our model with other restrictions on the memory and how the choice of negative counterexamples (arbitrary, or least, or the ones which are bounded by the largest positive datum seen so far) can impact automatic learnability. |
| abstract_unstemmed |
We introduce and study a model for learning in the limit by finite automata from positive data and negative counterexamples. The focus is on learning classes of languages with the membership problem computable by finite automata (so-called automatic classes). We show that, within the framework of our model, finite automata (automatic learners) can learn all automatic classes when memory of a learner is restricted by the size of the longest datum seen so far. We also study capabilities of automatic learners in our model with other restrictions on the memory and how the choice of negative counterexamples (arbitrary, or least, or the ones which are bounded by the largest positive datum seen so far) can impact automatic learnability. |
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Automatic learning from positive data and negative counterexamples |
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https://doi.org/10.1016/j.ic.2017.05.002 |
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Kinber, Efim Stephan, Frank |
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