A theory of the learnable

Valiant, L. G.·1984·Communications of the ACM

Cited by (1,004)

Items 501–600 of 1,004

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  3. Understanding Probabilistic Classifiers
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  4. Improved Learning of AC0 Functions
    Merrick L. Furst, Jeffrey C. Jackson, Sean W. Smith · 1991 · Elsevier eBooks
  5. Bounding sample size with the Vapnik-Chervonenkis dimension
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  6. Face recognition using artificial neural network group-based adaptive tolerance (GAT) trees
    Ming Zhang, John Fulcher · 1996 · IEEE Transactions on Neural Networks
  7. Efficient distribution-free learning of probabilistic concepts
    Michael J. Kearns, Robert E. Schapire · 2002 · Annual Symposium on Foundations of Computer Science
  8. Case Study: French Motor Third-Party Liability Claims
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  11. Derandomization in Cryptography
    Boaz Barak, Shien Jin Ong, Salil Vadhan · 2007 · SIAM Journal on Computing
  12. Evolvability from learning algorithms
    Vitaly Feldman · 2008 · ACM Symposium on Theory of Computing (STOC)
  13. On the sample complexity of noise-tolerant learning
    Javed Alexander Aslam, Scott Evan Decatur · 1996 · Information Processing Letters
  14. THE QSM ALGORITHM AND ITS APPLICATION TO SOFTWARE BEHAVIOR MODEL INDUCTION
    Pierre Dupont, Bernard Lambeau, Christophe Damas, Axel van Lamsweerde · 2008 · Applied Artificial Intelligence
  15. PALO: a probabilistic hill-climbing algorithm
    Russell Greiner · 1996 · Artificial Intelligence
  16. Learning Automata from Ordered Examples
    Sara Porat, Jerome A. Feldman · 1991 · Machine Learning
  17. SPACE-BOUNDED LEARNING AND THE VAPNIK-CHERVONENKIS DIMENSION
    Sally Floyd · 1989 · Elsevier eBooks
  18. Compression, Significance and Accuracy
    Stephen Muggleton, Ashwin Srinivasan, Michael Bain · 1992 · Elsevier eBooks
  19. Inferring Finite Automata with Stochastic Output Functions and an Application to Map Learning
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  20. Inductive logic programming and learnability
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  21. Limitations of multi-layer perceptron networks - steps towards genetic neural networks
    Heinz Mühlenbein · 1990 · Parallel Computing
  22. The Maximum Latency and Identification of Positive Boolean Functions
    Kazuhisa Makino, Toshihide Ibaraki · 1997 · SIAM Journal on Computing
  23. Divide-and-conquer learning and modular perceptron networks
    Hsin-Chia Fu, Yen-Po Lee, Cheng‐Chin Chiang, Hsiao-Tien Pao · 2001 · IEEE Transactions on Neural Networks
  24. Bridging the gap between theory and practice of approximate Bayesian inference
    Johan Kwisthout, Iris van Rooij · 2013 · Cognitive Systems Research
  25. Picking on the family: Disrupting android malware triage by forcing misclassification
    Alejandro Calleja, Alejandro G. Martín, Héctor D. Menéndez, Juan Tapiador, David Clark · 2017 · Expert Systems with Applications
  26. Making the Most of Your Samples
    Zhiyi Huang, Yishay Mansour, Tim Roughgarden · 2018 · SIAM Journal on Computing
  27. Lower bounds in pattern recognition and learning
    Luc Devroye, Gábor Lugosi · 1995 · Pattern Recognition
  28. Interpolation and Approximation of Sparse Multivariate Polynomials over $GF(2)$
    Ron M. Roth, Gyora M. Benedek · 1991 · SIAM Journal on Computing
  29. Statistical Machine Learning in Model Predictive Control of Nonlinear Processes
    Zhe Wu, David Rincón, Quanquan Gu, Panagiotis D. Christofides · 2021 · Mathematics
  30. A neuroidal architecture for cognitive computation
    Leslie Gabriel Valiant · 2000 · Journal of the ACM
  31. Pitfalls and protocols of data science in manufacturing practice
    Chia‐Yen Lee, Chen-Fu Chien · 2020 · Journal of Intelligent Manufacturing
  32. Phase Transitions in Relational Learning
    Attilio Giordana, Lorenza Saitta · 2000 · Machine Learning
  33. Learning Regular Languages from Simple Positive Examples
    François Denis · 2001 · Machine Learning
  34. Polynomial learnability and Inductive Logic Programming: Methods and results
    William W. Cohen, C. David Page · 1995 · New Generation Computing
  35. Efficient data reduction with EASE
    Hervé Brönnimann, Bin Chen, Manoranjan Dash, Peter J. Haas, Peter Scheuermann · 2003 · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)
  36. Deterministic Design for Neural Network Learning: An Approach Based on Discrepancy
    Cristiano Cervellera, Marco Muselli B · 2004 · IEEE Transactions on Neural Networks
  37. Bounds for the Computational Power and Learning Complexity of Analog Neural Nets
    Wolfgang Maass · 1993 · SIAM Journal on Computing
  38. Finding minimal generalizations for unions of pattern languages and its application to inductive inference from positive data
    Hiroki Arimura, Takeshi Shinohara, Setsuko Otsuki · 1994 · Lecture notes in computer science
  39. Learning via queries
    William I. Gasarch, Carl H. Smith · 1992 · Journal of the ACM
  40. Rank-r decision trees are a subclass of r-decision lists
    Avrim L. Blum · 1992 · Information Processing Letters
  41. Performance and Efficiency of Memetic Pittsburgh Learning Classifier Systems
    Jaume Bacardit, Natalio Krasnogor · 2009 · Evolutionary Computation
  42. An experimental study of the learnability of congestion control
    Anirudh Sivaraman, Keith Winstein, Pratiksha Thaker, Hari Balakrishnan · 2014 · ACM SIGCOMM Computer Communication Review
  43. Learning Qualitative Models of Dynamic Systems
    David Tak-Wai Hau, Enrico W Coiera · 1997 · Machine Learning
  44. Learning to Recognize 3D Objects with SNoW
    Ming–Hsuan Yang, Dan Roth, Narendra Ahuja · 2000 · Lecture notes in computer science
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    M. Li · 2002 · Annual Symposium on Foundations of Computer Science
  46. Hardness of approximate two-level logic minimization and PAC learning with membership queries
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  47. Learning From Entailment: An Application to Propositional Horn Sentences
    Michael Frazier, Leonard Pitt · 1993 · Elsevier eBooks
  48. Bias, Version Spaces and Valiant's Learning Framework
    David H. Haussler · 1987 · Elsevier eBooks
  49. PresenceSense
    Ming Jin, Ruoxi Jia, Zhaoyi Kang, Ioannis C. Konstantakopoulos, Costas J. Spanos · 2014 · ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys)
  50. Simultaneous Private Learning of Multiple Concepts
    Mark Bun, Kobbi Nissim B, Uri Stemmer · 2016 · Conference on Innovations in Theoretical Computer Science
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    Simon Burton, Benjamin Herd · 2023 · Frontiers in Computer Science
  52. Characterizing the sample complexity of private learners
    Amos Beimel, Kobbi Nissim B, Uri Stemmer · 2013 · Conference on Innovations in Theoretical Computer Science
  53. Learning-with-errors problem is easy with quantum samples
    Alex B. Grilo, Iordanis Kerenidis, Timo Zijlstra · 2019 · Physical Review A
  54. Randomly Fallible Teachers: Learning Monotone DNF with an Incomplete Membership Oracle
    Dana Angluin, Donna K. Slonim · 1994 · Machine Learning
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    Atithi Acharya, Siddhartha Saha, Anirvan M. Sengupta · 2021 · Physical Review A
  56. Acoustic Seabed Classification Based on Multibeam Echosounder Backscatter Data Using the PSO-BP-AdaBoost Algorithm: A Case Study From Jiaozhou Bay, China
    Xue Ji, Bisheng Yang, Qiuhua Tang · 2020 · IEEE Journal of Oceanic Engineering
  57. The Logic of Graph Neural Networks
    Martin Grohe · 2021 · ACM/IEEE Symposium on Logic in Computer Science (LICS)
  58. Generalization Error Bounds Using Unlabeled Data
    Matti Kääriäinen · 2005 · Lecture notes in computer science
  59. Training Artificial Neural Networks to Perform Rainfall Disaggregation
    Steven John Burian, S. Rocky Durrans, Stephan J. Nix, Robert E. Pitt · 2001 · Journal of Hydrologic Engineering
  60. Learnability beyond AC 0
    Jeffrey C. Jackson, Adam R. Klivans, Rocco A. Servedio · 2002 · ACM Symposium on Theory of Computing (STOC)
  61. Boosting and Microarray Data
    Philip M. Long, Vinsensius Berlian Vega · 2003 · Machine Learning
  62. Computational models of syntactic acquisition
    Charles D Yang · 2011 · Wiley Interdisciplinary Reviews Cognitive Science
  63. Ensemble Learning-Based Rate-Distortion Optimization for End-to-End Image Compression
    Yefei Wang, Dong Liu, Siwei Ma, Feng Wu, Wen Gao · 2020 · IEEE Transactions on Circuits and Systems for Video Technology
  64. Rainfall Disaggregation Using Artificial Neural Networks
    Steven John Burian, S. Rocky Durrans, Saša Tomić, Russell Lee Pimmel, Chung Ngai Wai · 2000 · Journal of Hydrologic Engineering
  65. Applying MDL to learn best model granularity
    Qiong Gao, Ming Li, Paul M. B. Vitanyi · 2000 · Artificial Intelligence
  66. Extremes in the degrees of inferability
    Lance Fortnow, William I. Gasarch, Sanjay K. Jain, Efim B. Kinber, Martin Kummer, Stuart A. Kurtz, Mark Pleszkovich, Theodore A. Slaman, Robert M Solovay, Frank Stephan · 1994 · Annals of Pure and Applied Logic
  67. One model for the learning of language
    Yang, Yuan, Piantadosi, Steven T. · 2022 · Proceedings of the National Academy of Sciences
  68. Maximizing strength of digital watermarks using neural networks
    K. Davis, Kayvan Najarian · 2002 · International Joint Conference on Neural Networks (IJCNN)
  69. Generative learning structures and processes for generalized connectionist networks
    Vasant Honavar, Leonard Uhr · 1993 · Information Sciences
  70. Learning to Recognize Three-Dimensional Objects
    Dan Roth, Ming–Hsuan Yang, Narendra Ahuja · 2002 · Neural Computation
  71. Boosting and Hard-Core Set Construction
    Adam R. Klivans, Rocco A. Servedio · 2003 · Machine Learning
  72. An efficient membership-query algorithm for learning DNF with respect to the uniform distribution
    Jeffrey C. Jackson · 2002 · Annual Symposium on Foundations of Computer Science
  73. Active Learning in Neural Networks
    Martina Hasenjäger, Helge Joachim Ritter · 2002 · Studies in fuzziness and soft computing
  74. Research on modulation recognition with ensemble learning
    Tong Liu, Guan Yanan, Yun Lin · 2017 · EURASIP Journal on Wireless Communications and Networking
  75. Uncertainty and computational complexity
    Peter L. Bossaerts, Nitin Yadav, Carsten Murawski · 2018 · Philosophical Transactions of the Royal Society B Biological Sciences
  76. Deep learning the hyperbolic volume of a knot
    Vishnu Jejjala, Arjun Kar, Onkar Parrikar · 2019 · Physics Letters B
  77. Classification on Soft Labels Is Robust against Label Noise
    Christian Thiel · 2008 · Lecture notes in computer science
  78. On learning monotone DNF under product distributions
    Rocco A. Servedio · 2004 · Information and Computation
  79. Learning with a slowly changing distribution
    Peter L. Bartlett · 1992 · Conference on Learning Theory
  80. On-line confidence machines are well-calibrated
    Vladimir Vovk · 2003 · Annual Symposium on Foundations of Computer Science
  81. A Learning Criterion for Stochastic Rules
    Kenji Yamanishi · 1992 · Machine Learning
  82. Learning DFA from Simple Examples
    Rajesh Girish Parekh, Vasant Honavar · 2001 · Machine Learning
  83. On the complete determination of biological systems
    Douglas W. Selinger, Matthew A. Wright, George McDonald Church · 2003 · Trends in biotechnology
  84. Interactive learning of monotone Boolean functions
    Boris Kovalerchuk, Evangelos Triantaphyllou, Aniruddha S. Deshpande, Evgenii E. Vityaev · 1996 · Information Sciences
  85. Adaptive acquisition of language
    Allen Louis Gorin, S. Levinson, A.N. Gertner, E.R. Goldman · 1991 · Computer Speech & Language
  86. Models of dataset size, question design, and cross-language speech perception for speech crowdsourcing applications
    Mark Hasegawa‐Johnson, Jennifer Cole, Preethi Jyothi, Lav R. Varshney · 2015 · Laboratory Phonology Journal of the Association for Laboratory Phonology
  87. MapReduce-based Backpropagation Neural Network over large scale mobile data
    Zhi-Qiang Liu, Hongyan Li, Gaoshan Miao · 2010 · International Conference on Natural Computation (ICNC)
  88. BET on Independence
    Kai Zhang · 2019 · Journal of the American Statistical Association
  89. TeLEx: learning signal temporal logic from positive examples using tightness metric
    Susmit Jha, Ashish Tiwari, Sanjit Arunkumar Seshia, Tuhin Sahai, Natarajan Shankar · 2019 · Formal Methods in System Design
  90. Boosting and hard-core sets
    Adam R. Klivans, Rocco A. Servedio · 2003 · Annual Symposium on Foundations of Computer Science
  91. Canalization and Control in Automata Networks: Body Segmentation in Drosophila melanogaster
    Manuel Marques-Pita, Luis Mateus Rocha · 2013 · PLoS ONE
  92. Artificial Intelligence and Machine Learning in Bioinformatics
    Kaitao Lai, Natalie A. Twine, Aidan R. O’Brien, Yi Guo, Denis C. Bauer · 2018 · Elsevier eBooks
  93. Learnability
    Jeffrey Nicholas Heinz, Jason Riggle · 2011 · Wiley eBooks
  94. Loading Deep Networks Is Hard
    Jiřı́ Šı́ma · 1994 · Neural Computation
  95. Belief Polarization in a Complex World: A Learning Theory Perspective
    Nika Haghtalab, Matthew O. Jackson, Ariel D. Procaccia · 2020 · Proceedings of the National Academy of Sciences
  96. Architectures for neural networks as surrogates for dynamic systems in chemical engineering
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  98. Classification Trees and Rule-Based Models
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  99. Support vector machine ensembles for breast cancer type prediction from mid-FTIR micro-calcification spectra
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  100. A Comparative Analysis of Recent Identification Approaches for Discrete‐Event Systems
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