| 1993 | |  | Bivariate Scientific Function Finding in a Sampled, Real-Data Testbed - Cullen Schaffer |
| | | Learning and robust learning of product distributions - K. Höffgen |
| | | Composite Geometric Concepts and Polynomial Predictability - P. M. Long and M. K. Warmuth |
| | | Computational Limits on Team Identification of Languages - S. Jain and A. Sharma |
| | | Efficient learning of typical finite automata from random walks - Y. Freund, M. Kearns, D. Ron, R. Rubinfeld, R. Schapire and L. Sellie |
| | | The Probably Approximately Correct PAC and Other Learning Models - D. Haussler and M. K. Warmuth |
| | | Lower bounds for PAC learning with queries - G. Turán |
| | | The sample complexity of consistent learning with one-sided error - E. Takimoto and A. Maruoka |
| | | Using Genetic Algorithms for Concept Learning - Kenneth A. De Jong, William M. Spears and Diana F. Gordon |
| | | On polynomial-time probably almost discriminative learnability - K. Yamanishi |
| | | The Learnability of Recursive Languages in Dependence on the Hypothesis Space - S. Lange and T. Zeugmann |
| | | Exact learning via the monotone theory - Nader H. Bshouty |
| | | Efficient noise-tolerant learning from statistical queries - M. Kearns |
| | | Polynomial learnability of linear threshold approximations - T. Bylander |
| | | Selecting a Classification Method by Cross-Validation - Cullen Schaffer |
| | | Use of reduction arguments in determining Popperian FIN-type learning capabilities - R. Daley and B. Kalyanasundaram |
| | | A Reply to Hellerstein’s Book Review of Machine Learning: A Theoretical Approach - B. K. Natarajan |
| | | Erratum to Discovery - Authorless |
| | | The stastical mechanics of learning a rule - T. L. H. Watkin, A. Rau and M. Biehl |
| | | Balanced Cooperative Modeling - Katharina Morik |
| | | Learnability of Recursive, Non-determinate Theories: Some Basic Results and Techniques - M. Frazier and C. D. Page |
| | | Monotonic Versus Non-monotonic Language Learning - S. Lange and T. Zeugmann |
| | | An Introduction to Kolmogorov Complexity and Its Applications - M. Li and P. Vitányi |
| | | Lower bounds on the Vapnik-Chervonenkis dimension of multi-layer threshold networks - P. Bartlett |
| | | General bounds on statistical query learning and PAC learning with noise via hypothesis boosting - Javed A. Aslam and Scott E. Decatur |
| | | Amplification of weak learning under the uniform distribution - D. Boneh and R. Lipton |
| | | Prioritized Sweeping: Reinforcement Learning With Less Data and Less Time - Andrew W. Moore and Christopher G. Atkeson |
| | | Uniform characterizations of various kinds of language learning - S. Kapur |
| | | Multistrategy Learning and Theory Revision - Lorenza Saitta, Marco Botta and Filippo Neri |
| | | Inductive resolution - T. Sato and S. Akiba |
| | | Synthesis of UNIX Programs Using Derivational Analogy - Sanjay Bhansali and Mehdi T. Harandi |
| | | A typed -calculus for proving-by-example and bottom-up generalization procedure - M. Hagiya |
| | | Learnability: Admissible, Co-finite and Hypersimple Sets - G. Baliga and J. Case |
| | | Wastewater Treatment Systems from Case-Based Reasoning - Srinivas Krovvidy and William G. Wee |
| | | Induction of probabilistic rules based on rough set theory - S. Tsumoto and H. Tanaka |
| | | Language learning in dependence on the space of hypotheses - S. Lange and T. Zeugmann |
| | | Unifying learning methods by colored digraphs - K. Yoshida, H. Motoda and N. Indurkhya |
| | | Generalized unification as background knowledge in learning logic programs - A. Yamamoto |
| | | -approximations of k-label spaces - S. Hasegawa, H. Imai and M. Ishiguro |
| | | Pac-Learning a Restricted Class of Recursive Logic Programs - William Cohen |
| | | How to invent characterizable inference methods for regular languages - T. Knuutila |
| | | On the Learnability of Disjunctive Normal Form Formulas and Decision Trees. - H. Aizenstein |
| | | Case-Based Representation and Learning of Pattern Languages - K. P. Jantke and S. Lange |
| | | A Reply to Cohen’s Book Review of Creating a Memory of Causal Relationships - Michael Pazzani |
| | | Efficient identification of regular expressions from representative examples - A. Brāzma |
| | | Keeping neural networks simple by minimizing the description length of the weights - G. Hinton and D. van Camp |
| | | Learning Causal Patterns: Making a Transition from Data-Driven to Theory-Driven Learning - Michael Pazzani |
| | | Learning theory toward genome informatics - S. Miyano |
| | | The ‘lob-pass’ problem and an on-line learning model of rational choice - N. Abe and J. Takeuchi |
| | | Algebraic structure of some learning systems - J.-G. Ganascia |
| | | Some computational lower bounds for the computational complexity of inductive logic programmming - Jorg-Uwe Kietz |
| | | Machine Learning: A Theoretical Approach - Lisa Hellerstein |
| | | An on-line algorithm for improving performance in navigation. - A. Blum and P. Chalasani |
| | | Coding Decision Trees - C. S. Wallace and J. D. Patrick |
| | | Extracting Refined Rules from Knowledge-Based Neural Networks - Geoffrey G. Towell and Jude W. Shavlik |
| | | An Analysis of the WITT Algorithm - Jan L. Talmon, Herco Fonteijn and Peter J. Braspenning |
| | | On the power of sigmoid neural networks - J. Kilian and H. Siegelmann |
| | | Learning strategies using decision lists - S. Kobayashi |
| | | Infinitary Self-Reference in Learning Theory - J. Case |
| | | On the complexity of learning strings and sequences - T. Jiang and M. Li |
| | | Overfitting Avoidance as Bias - Cullen Schaffer |
| | | Machine Learning: From Theory to Applications; Cooperative Research at Siemens and MIT - S. J. Hanson and W. Remmele |
| | | On the power of polynomial discriminators and radial basis function networks - M. Anthony and S. Holden |
| | | Average case analysis of the clipped Hebb rule for nonoverlapping perceptron networks - M. Golea and M. Marchand |
| | | Can complexity theory benefit from learning theory? Extended Abstract - T. Hegedűs |
| | | The minimum consistent DFA problem cannot be approximated within any polynomial - L. Pitt and M. Warmuth |
| | | Learning with the Knowledge of an Upper Bound on Program Size - S. Jain and A. Sharma |
| | | Identifying and using patterns in sequential data - P. Laird |
| | | Explanation-Based Learning for Diagnosis - Yousri El Fattah and Paul O’Rorke |
| | | Creating a Memory of Casual Relationships - William W. Cohen |
| | | On-line learning of functions of bounded variation under various sampling schemes - S. E. Posner and S. R. Kulkarni |
| | | Experience Selection and Problem Choice in an Exploratory Learning System - Paul D. Scott and Shaul Markovitch |
| | | Using Knowledge-Based Neural Networks to Improve Algorithms: Refining the Chou-Fasman Algorithm for Protein Folding - Richard Maclin and Jude W. Shavlik |
| | | Competition-Based Induction of Decision Models from Examples - David Perry Greene and Stephen F. Smith |
| | | Plausible Justification Trees: A Framework for Deep and Dynamic Integration of Learning Strategies - Gheorghe Tecuci |
| | | Conservativeness and monotonicity for learning algorithms - E. Takimoto and A. Maruoka |
| | | Learning fallible finite state automata - D. Ron and R. Rubinfeld |
| | | Cryptographic Limitations on Learning One-Clause Logic Programs - William Cohen |
| | | Convergence properties of the EM approach to learning in mixture-of-experts architectures - M. I. Jordan and L. Xu |
| | | Optimal layered learning: a PAC approach to incremental sampling - S. Muggleton |
| | | On the VC-dimension of depth four threshold circuits and the complexity of Boolean-valued functions - A. Sakurai |
| | | Cost-Sensitive Learning of Classification Knowledge and Its Applications in Robotics - Ming Tan |
| | | Opportunism and Learning - Kristian Hammond et al. |
| | | Capabilities of probabilistic learners with bounded mind changes - R. Daley and B. Kalyanasundaram |
| | | The Design of Discrimination Experiments - Shankar A. Rajamoney |
| | | Indexing and Elaboration and Refinement: Incremental Learning of Explanatory Cases - Ashwin Ram |
| | | Probability is more powerful than team for language identification from positive data - S. Jain and A. Sharma |
| | | On the duality between mechanistic learners and what it is they learn - R. Freivalds and C. H. Smith |
| | | Discovery as Autonomous Learning from the Environment - Wei-Min Shen |
| | | H^ Optimality of the LMS Algorithm - B. Hassibi, A. H. Sayed and T. Kailath |
| | | On bounded queries and approximation - Richard Chang and William I. Gasarch |
| | | Learning k decision trees on the uniform distribution - T. Hancock |
| | | Information Filtering: Selection Mechanisms in Learning Systems - Shaul Markovitch and Paul D. Scott |
| | | An Integrated Framework for Empirical Discovery - Bernd Nordhausen and Pat Langley |
| | | Learning in the presence of malicious errors - M. Kearns and M. Li |
| | | How to use expert advice - N. Cesa-Bianchi, Y. Freund, D. P. Helmbold, D. Haussler, R. E. Schapire and M. K. Warmuth |
| | | Learning two-tape automata from queries and counterexamples - T. Yokomori |
| | | Induction Over the Unexplained: Using Overly-General Domain Theories to Aid Concept Learning - Raymond J. Mooney |
| | | Cryptographic hardness of distribution-specific learning - M. Kharitonov |
| | | Thue systems and DNA - a learning algorithm for a subclass - R. Siromoney, D. G. Thomas, K. G. Subramanian and V. R. Dare |
| | | Capabilities of fallible FINite learning - R. Daley, B. Kalyanasundaram and M. Velauthapillai |
| | | On learning in the limit and non-uniform , - learning - S. Ben-David and M. Jacovi |
| | | Linear time deterministic learning of k-term DNF - U. Berggren |
| | | Towards efficient inductive synthesis of expressions from input/output examples - J. Barzdins, G. Barzdins, K. Apsitis and U. Sarkans |
| | | Parameterized learning complexity - R. Downey, P. Evans and M. Fellows |
| | | Complexity of computing Vapnik-Chervonenkis dimension - A. Shinohara |
| | | Localization vs. identification of semi-algebraic sets - S. Ben-David and M. Lindenbaum |
| | | Choosing a reliable hypothesis - W. Evans, S. Rajagopalan and U. Vazirani |
| | | A Knowledge-Intensive Genetic Algorithm for Supervised Learning - Cezary Z. Janikow |
| | | On the query complexity of learning - S. Kannan |
| | | Scale-sensitive dimensions, uniform convergence, and learnability - Noga Alon, Shai Ben-David and Nicolòand Haussler David Cesa-Bianchi |
| | | Design Methods for Scientific Hypothesis Formation and Their Application to Molecular Biology - Peter D. Karp |
| | | Learning Recursive Languages With a Bounded Number of Mind Changes - S. Lange and T. Zeugmann |
| | | Learning unions of two rectangles in the plane with equivalence queries - Z. Chen |
| | | Inferential Theory of Learning as a Conceptual Basis for Multistrategy Learning - Ryszard S. Michalski |
| | | Machine Discovery of Effective Admissible Heuristics - Armand E. Prieditis |
| | | Learning read-once formulas with queries - D. Angluin, L. Hellerstein and M. Karpinski |
| | | On the role of procrastination for machine learning - R. Freivalds and C. H. Smith |
| | | The VC dimensions of finite automata with n states - Y. Ishigami and S. Tani |
| | | Rate of approximation results motivated by robust neural network learning - C. Darken, M. Donahue, L. Gurvits and E. Sontag |
| | | On the Impact of Order Independence to the Learnability of Recursive Languages - S. Lange and T. Zeugmann |
| | | On-line learning with linear loss constraints - N. Littlestone and P. Long |
| | | What Makes a Problem Hard for a Genetic Algorithm? Some Anomalous Results and Their Explanation - Stephanie Forrest and Melanie Mitchell |
| | | Bounds for the computational power and learning complexity of analog neural nets - W. Maass |
| | | On-line learning of rectangles in noisy environments - P. Auer |
| | | Occam’s razor for functions - B. K. Natarajan |
| | | Inductive inference machines that can refute hypothesis spaces - Y. Mukouchi and S. Arikawa |
| | | Genetic Reinforcement Learning for Neurocontrol Problems - Darrell Whitley et al. |
| | | Learning an intersection of k halfspaces over a uniform distribution - Avrim Blum and Ravi Kannan |
| | | A decomposition based induction model for discovering concept clusters - N. Zhong and S. Ohsuga |
| | | Active Learning Using Arbitrary Binary Valued Queries - S. R. Kulkarni, S. K. Mitter and J. N. Tsitsiklis |
| | | Worst-case quadratic loss bounds for a generalization of the Widrow-Hoff rule - N. Cesa-Bianchi, P. Long and M. Warmuth |
| | | Derivational Analogy in Prodigy: Automating Case Acquisition, Storage, and Utilization - Manuela M. Veloso and Jaime G. Carbonell |
| | | Learning Decision Trees using the Fourier Spectrum - E. Kushilevitz and Y. Mansour |
| | | Algorithmisches Lernen von Funktionen und Sprachen - T. Zeugmann |
| | | A model of sequence extrapolation - P. Laird, S. R and P. Dunning |
| | | A Weighted Nearest Neighbor Algorithm for Learning with Symbolic Features - Scott Cost and Steven Salzberg |
| | | Learning with restricted focus of attention - S. Ben-David and E. Dichterman |
| | | Introduction: Cognitive Autonomy in Machine Discovery - Jan M. Żytkow |
| | | On the non-existence of maximal inference degrees for language identification - S. Jain and A. Sharma |
| | | Acceleration of learning in binary choice problems - Y. Kabashima and S. Shinomoto |
| | | Finiteness results for sigmoid - A. Macintyre and E. D. Sontag |
| | | Very Simple Classification Rules Perform Well on Most Commonly Used Datasets - Robert C. Holte |
| | | C4.5: Programs for machine learning - J. R. Quinlan |
| | | Scale-sensitive dimensions, uniform convergence, and learnability - Noga Alon, Shai Ben-David, Nicolò Cesa-Bianchi and David Haussler |
| | | Properties of language classes with finite elasticity - T. Moriyama and M. Sato |
| | | Research Note on Decision Lists - Ron Kohavi and Scott Benson |
| | | A new view of the EM algorithm that justifies incremental and other variants - R. M. Neal and G. E. Hinton |
| | | Learning -branching programs with queries - V. Raghavan and D. Wilkins |
| | | Learning with growing quality - J. Viksna |
| | | On learning embedded symmetric concepts - A. Blum, P. Chalasani and J. Jackson |
| | | A new algorithm for automatic configuration of hidden Markov models - M. Iwayama, N. Indurkhya and H. Motoda |
| | | Editorial on expanding to twelve issues a year - Thomas Dietterich |
| | | Reformulation of explanation by linear logic - toward logic for explanation - J. Arima and H. Sawamura |
| | | Integrating Feature Extraction and Memory Search - Christopher Owens |
| | | Exact learning of linear combinations of monotone terms from function value queries - A. Nakamura and N. Abe |
| | | A perceptual criterion for visually controlling learning - M. Suwa and H. Motoda |
| | | Discovery by Minimal Length Encoding: A Case Study in Molecular Evolution - Aleksandar Milosavljević and Jerzy Jurka |
| | | Rates of convergence for minimum contrast estimators - L. Birge and P. Massart |
| | | Learning with Minimal Number of Queries - S. Matar |
| | | Statistical queries and faulty PAC oracles - S. E. Decatur |
| | | Genetic algorithms and machine learning - J. Grefenstette |
| | | Neural discriminant analysis - J. R. Cuellar and H. U. Simon |
| | | On probably correct classification of concepts - S. Kulkarni and O. Zeitouni |
| | | Language Learning with a Bounded Number of Mind Changes - S. Lange and T. Zeugmann |
| | | On the average tractability of binary integer programming and the curious transition to perfect generalization in learning majority functions - S. Fang and S. Venkatesh |
| | | Noise-Tolerant Occam Algorithms and Their Applications to Learning Decision Trees - Yasubumi Sakakibara |
| January | | Information bounds for the risk of Bayesian predictions and the redundancy of universal codes - A. Barron, B. Clarke and D. Haussler |
| | | Searching in an Unknown Environment: An Optimal Randomized Algorithm for the Cow-Path Problem - M. Kao, J. H. Reif and S. R. Tate |
| | | Infinitary Self Reference in Learning Theory - J. Case |
| February | | Pattern Recognition and Valiant’s Learning Framework - L. Saitta and F. Bergadano |
| April | | Inference of Finite Automata using Homing Sequences - R. L. Rivest and R. E. Schapire |
| June | | Learning from entailment: an application to propositional Horn sentences - Michael Frazier and Leonard Pitt |
| July | | Using Dirichlet Mixture Priors to Derive Hidden Markov Models for Protein Families - M. P. Brown, R. Hughey, A. Krogh, I. S. Mian, K. Sjölander and D. Haussler |
| August | | Exact identification of circuits using fixed points of amplification functions - S. A. Goldman, M. J. Kearns and R. E. Schapire |
| October | | Learning binary relations and total orders - S. A. Goldman, R. L. Rivest and R. E. Schapire |
| | | Not-So-Nearly-Minimal-Size Program Inference - J. Case, S. Mandayam and S. Jain |
| December | | Recursive linear estimation in Krein spaces - part I: Theory - B. Hassibi, A. H. Sayed and T. Kailath |
