Machine learning an artificial intelligence approach volume II
Machine learning an artificial intelligence approach volume II
Handbook of AI
The complexity of theorem-proving procedures
STOC '71 Proceedings of the third annual ACM symposium on Theory of computing
STOC '87 Proceedings of the nineteenth annual ACM symposium on Theory of computing
Information retrieval from hypertext: update on the dynamic medical handbook project
HYPERTEXT '89 Proceedings of the second annual ACM conference on Hypertext
Analysis of upper bound in Valiant's model for learning bounded CNF expressions
SAC '93 Proceedings of the 1993 ACM/SIGAPP symposium on Applied computing: states of the art and practice
On the Fourier spectrum of monotone functions
STOC '95 Proceedings of the twenty-seventh annual ACM symposium on Theory of computing
Sim: a utility for detecting similarity in computer programs
SIGCSE '99 The proceedings of the thirtieth SIGCSE technical symposium on Computer science education
Boosting to correct inductive bias in text classification
Proceedings of the eleventh international conference on Information and knowledge management
An Architecture of a Web-Based Collaborative Image Search Engine
On the Move to Meaningful Internet Systems, 2002 - DOA/CoopIS/ODBASE 2002 Confederated International Conferences DOA, CoopIS and ODBASE 2002
Query-preserving watermarking of relational databases and XML documents
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
MEGA---the maximizing expected generalization algorithm for learning complex query concepts
ACM Transactions on Information Systems (TOIS)
Guest editorial: Learning theory
Machine Learning
Discriminative learning can succeed where generative learning fails
Information Processing Letters
Inductive Synthesis of Functional Programs: An Explanation Based Generalization Approach
The Journal of Machine Learning Research
Exact learning of random DNF over the uniform distribution
Proceedings of the forty-first annual ACM symposium on Theory of computing
Cross-task knowledge-constrained self training
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Empirical Study of Relational Learning Algorithms in the Phase Transition Framework
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
Differential privacy: a survey of results
TAMC'08 Proceedings of the 5th international conference on Theory and applications of models of computation
ROLEX-SP: Rules of lexical syntactic patterns for free text categorization
Knowledge-Based Systems
Combinatorial shell bounds for generalization ability
Pattern Recognition and Image Analysis
Query-preserving watermarking of relational databases and Xml documents
ACM Transactions on Database Systems (TODS)
Discriminative learning can succeed where generative learning fails
COLT'06 Proceedings of the 19th annual conference on Learning Theory
Linguistic categorization and complexity
SIGMORPHON '12 Proceedings of the Twelfth Meeting of the Special Interest Group on Computational Morphology and Phonology
PAC-Learning with general class noise models
KI'12 Proceedings of the 35th Annual German conference on Advances in Artificial Intelligence
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Humans appear to be able to learn new concepts without needing to be programmed explicitly in any conventional sense. In this paper we regard learning as the phenomenon of knowledge acquisition in the absence of explicit programming. We give a precise methodology for studying this phenomenon from a computational viewpoint. It consists of choosing an appropriate information gathering mechanism, the learning protocol, and exploring the class of concepts that can be learnt using it in a reasonable (polynomial) number of steps. We find that inherent algorithmic complexity appears to set serious limits to the range of concepts that can be so learnt. The methodology and results suggest concrete principles for designing realistic learning systems.