A theory of the learnable

Valiant, L. G.·1984·Communications of the ACM
DOI 10.1145/1968.1972

Cited by (1,004)

Items 901–1,000 of 1,004

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  20. Learning logic programs with structured background knowledge☆☆An extended abstract of this paper appeared in: L. De Raedt (Ed.), Proceedings of the Fifth International Workshop on Inductive Logic Programming, Tokyo, Japan, 1995, pp. 53–76, Scientific Report of the Department of Computer Science, Katholieke Universiteit Leuven, and also in the post-conference volume: L. De Raedt (Ed.), Advances in Inductive Logic Programming, IOS Press, Amsterdam/Ohmsha, Tokyo, 1996, pp. 172–191.
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    Wen-Guey Tzeng · 1992 · Machine Learning
  60. Software Validation by Means of Statistical Testing: Retrospect and Future Direction
    Pascale Thévenod-Fosse · 1991 · Dependable computing and fault-tolerant systems
  61. Learning DNF by approximating inclusion-exclusion formulae
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  62. Computer systems that learn: an empirical study of the effect of noise on the performance of three classification methods
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  63. Logical analysis of binary data with missing bits
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  64. Learning Fallible Deterministic Finite Automata
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  65. Defaults and relevance in model-based reasoning
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  66. On learning Decision Committees
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  67. Learning by canonical smooth estimation. I. Simultaneous estimation
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  68. Learning regular languages from counterexamples
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  70. On the efficient allocation of resources for hypothesis evaluation: a statistical approach
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  71. Exact learning of read- k disjoint DNF and not-so-disjoint DNF
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  72. LEARNING NESTED DIFFERENCES OF INTERSECTION-CLOSED CONCEPT CLASSES
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  76. Efficient Pseudorandom Functions via On-the-Fly Adaptation
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  78. A Bayesian Optimization Framework for Neural Network Compression
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  80. Genetic Algorithms and Machine Learning for Predicting Surface Composition, Structure, and Chemistry: A Historical Perspective and Assessment
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  83. From Hopfield nets to recursive networks to graph machines: Numerical machine learning for structured data
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  85. A Polynomial Time Algorithm for Lossy Population Recovery
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  99. Learning DFA from simple examples
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  100. Inductive logic programming: Derivations, successes and shortcomings
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