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* |