Learning sparse multivariate polynomials over a field with queries and counterexamples
COLT '93 Proceedings of the sixth annual conference on Computational learning theory
On learning visual concepts and DNF formulae
COLT '93 Proceedings of the sixth annual conference on Computational learning theory
COLT '95 Proceedings of the eighth annual conference on Computational learning theory
A note on learning multivariate polynomials under the uniform distribution (extended abstract)
COLT '95 Proceedings of the eighth annual conference on Computational learning theory
Learning to reason with a restricted view
COLT '95 Proceedings of the eighth annual conference on Computational learning theory
Adaptive versus nonadaptive attribute-efficient learning
STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
More efficient PAC-learning of DNF with membership queries under the uniform distribution
COLT '99 Proceedings of the twelfth annual conference on Computational learning theory
A Winnow-Based Approach to Context-Sensitive Spelling Correction
Machine Learning - Special issue on natural language learning
An intelligent adaptive filtering agent based on an on-line learning model (poster abstract)
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Learning to Reason with a Restricted View
Machine Learning
Machine Learning
Adaptive Versus Nonadaptive Attribute-Efficient Learning
Machine Learning
Learning to recognize three-dimensional objects
Neural Computation
A Tale of Two Classifiers: SNoW vs. SVM in Visual Recognition
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Learning to Recognize 3D Objects with SNoW
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part I
PAC Meditation on Boolean Formulas
Proceedings of the 5th International Symposium on Abstraction, Reformulation and Approximation
A New Algorithm to Select Learning Examples from Learning Data
IDEAL '00 Proceedings of the Second International Conference on Intelligent Data Engineering and Automated Learning, Data Mining, Financial Engineering, and Intelligent Agents
A classification approach to word prediction
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Weakly supervised named entity transliteration and discovery from multilingual comparable corpora
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Named entity transliteration and discovery from multilingual comparable corpora
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
Interactive feature space construction using semantic information
CoNLL '09 Proceedings of the Thirteenth Conference on Computational Natural Language Learning
Relational learning for NLP using linear threshold elements
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Comparing learners for Boolean partitions: implications for morphological paradigms
CLAGI '09 Proceedings of the EACL 2009 Workshop on Computational Linguistic Aspects of Grammatical Inference
Learning with feature description logics
ILP'02 Proceedings of the 12th international conference on Inductive logic programming
Learning trees and rules with set-valued features
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Tractable feature generation through description logics with value and number restrictions
IEA/AIE'06 Proceedings of the 19th international conference on Advances in Applied Artificial Intelligence: industrial, Engineering and Other Applications of Applied Intelligent Systems
Online closure-based learning of relational theories
ILP'05 Proceedings of the 15th international conference on Inductive Logic Programming
Generalization behaviour of alkemic decision trees
ILP'05 Proceedings of the 15th international conference on Inductive Logic Programming
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This paper presents a theoretical model for learning Boolean functions in domains having a large, potentially infinite number of attributes. The model allows an algorithm to employ a rich vocabulary to describe the objects it encounters in the world without necessarily incurring time and space penalties so long as each individual object is relatively simple. We show that many of the basic Boolean functions learnable in standard theoretical models, such as conjunctions, disjunctions, K-CNF, and K-DNF, are still learnable in the new model, though by algorithms no longer quite so trivial as before. The new model forces algorithms for such classes to act in a manner that appears more natural for many learning scenarios.