5. COLT 1992: Pittsburgh, PA, USA

• Nader H. Bshouty, Thomas R. Hancock, Lisa Hellerstein:
  Learning Boolean Read-Once Formulas with Arbitrary Symmetric and Constant Fan-in Gates. 1-15
  
• Zhixiang Chen, Wolfgang Maass:
  On-line Learning of Rectangles. 16-28
  
• Michael Kharitonov:
  Cryptographic Lower Bounds for Learnability of Boolean Functions on the Uniform Distribution. 29-36
  
• Jyrki Kivinen, Heikki Mannila, Esko Ukkonen:
  Learning Hierarchical Rule Sets. 37-44
  
• Kevin J. Lang:
  Random DFA's Can Be Approximately Learned from Sparse Uniform Examples. 45-52
  
• Yishay Mansour:
  An O(n^{log log n}) Learning Algorithm for DNF Under the Uniform Distribution. 53-61
  
• Mihir Bellare:
  A Technique for Upper Bounding the Spectral Norm with Applications to Learning. 62-70
  
• Howard Aizenstein, Leonard Pitt:
  Exact Learning of Read-k Disjoint DNF and Not-So-Disjoint DNF. 71-76
  
• Sally A. Goldman, H. David Mathias:
  Learning k-Term DNF Formulas with an Incomplete Membership Oracle. 77-84
  
• Michele Flammini, Alberto Marchetti-Spaccamela, Ludek Kucera:
  Learning DNF Formulae Under Classes of Probability Distributions. 85-92
  
• Santosh S. Venkatesh, Robert R. Snapp, Demetri Psaltis:
  Bellman Strikes Again! The Growth Rate of Sample Complexity with Dimension for the Nearest Neighbor Classifier. 93-102
  
• Jyh-Han Lin, Jeffrey Scott Vitter:
  A Theory for Memory-Based Learning. 103-115
  
• William W. Cohen, Haym Hirsh:
  Learnability of Description Logics. 116-127
  
• Saso Dzeroski, Stephen Muggleton, Stuart J. Russell:
  PAC-Learnability of Determinate Logic Programs. 128-135
  
• Hiroki Arimura, Hiroki Ishizaka, Takeshi Shinohara:
  Polynomial Time Inference of a Subclass of Context-Free Transformations. 136-143
  
• Bernhard E. Boser, Isabelle Guyon, Vladimir Vapnik:
  A Training Algorithm for Optimal Margin Classifiers. 144-152
  
• Don Kimber, Philip M. Long:
  The Learning Complexity of Smooth Functions of a Single Variable. 153-159
  
• Ethan Bernstein:
  Absolute Error Bounds for Learning Linear Functions Online. 160-163
  
• Kenji Yamanishi:
  Probably Almost Discriminative Learning. 164-171
  
• Sanjeev R. Kulkarni, John N. Tsitsiklis, Sanjoy K. Mitter, Ofer Zeitouni:
  PAC Learning With Generalized Samples and an Application to Stochastic Geometry. 172-179
  
• Peter Cholak, Efim B. Kinber, Rodney G. Downey, Martin Kummer, Lance Fortnow, Stuart A. Kurtz, William I. Gasarch, Theodore A. Slaman:
  Degrees of Inferability. 180-192
  
• John Case, Sanjay Jain, Arun Sharma:
  On Learning Limiting Programs. 193-202
  
• Robert P. Daley, Bala Kalyanasundaram, Mahendran Velauthapillai:
  Breaking the Probability 1/2 Barrier in FIN-Type Learning. 203-217
  
• Klaus P. Jantke:
  Case-Based Learning in Inductive Inference. 218-223
  
• Rolf Wiehagen, Carl H. Smith:
  Generalization versus Classification. 224-230
  
• Avrim Blum, Prasad Chalasani:
  Learning Switching Concepts. 231-242
  
• Peter L. Bartlett:
  Learning With a Slowly Changing Distribution. 243-252
  
• Gyora M. Benedek, Alon Itai:
  Dominating Distributions and Learnability. 253-264
  
• Alberto Bertoni, Paola Campadelli, Anna Morpurgo, Sandra Panizza:
  Polynomial Iniform Convergence and Polynomial-Sample Learnability. 265-271
  
• Kevin Buescher, P. R. Kumar:
  Learning Stochastic Functions by Smooth Simultaneous Estimation. 272-279
  
• Ronny Meir, José F. Fontanari:
  On Learning Noisy Threshold Functions with Finite Precision Weights. 280-286
  
• H. Sebastian Seung, Manfred Opper, Haim Sompolinsky:
  Query by Committee. 287-294
  
• Yasubumi Sakakibara, Rani Siromoney:
  A Noise Model on Learning Sets of Strings. 295-302
  
• Shyam Kapur, Gianfranco Bilardi:
  Language Learning from Stochastic Input. 303-310
  
• Martin Anthony, Graham Brightwell, David A. Cohen, John Shawe-Taylor:
  On Exact Specification by Examples. 311-318
  
• Jeffrey Jackson, Andrew Tomkins:
  A Computational Model of Teaching. 319-326
  
• Kathleen Romanik:
  Approximate Testing and Learnability. 327-332
  
• Shai Ben-David, Nicolò Cesa-Bianchi, Philip M. Long:
  Characterizations of Learnability for Classes of {O, ..., n}-Valued Functions. 333-340
  
• Michael J. Kearns, Robert E. Schapire, Linda Sellie:
  Toward Efficient Agnostic Learning. 341-352
  
• Svetlana Anoulova, Paul Fischer, Stefan Pölt, Hans-Ulrich Simon:
  PAB-Decisions for Boolean and Real-Valued Features. 353-362
  
• Rusins Freivalds, Carl H. Smith:
  On the Role of Procrastination for Machine Learning. 363-376
  
• Steffen Lange, Thomas Zeugmann:
  Types of Monotonic Language Learning and Their Characterization. 377-390
  
• Yoav Freund:
  An Improved Boosting Algorithm and Its Implications on Learning Complexity. 391-398
  
• David P. Helmbold, Manfred K. Warmuth:
  Some Weak Learning Results. 399-412
  
• Neri Merhav, Meir Feder:
  Universal Sequential Learning and Decision from Individual Data Sequences. 413-427
  
• Klaus-Uwe Höffgen, Hans-Ulrich Simon:
  Robust Trainability of Single Neurons. 428-439
  
• Hava T. Siegelmann, Eduardo D. Sontag:
  On the Computational Power of Neural Nets. 440-449
  
• Robert H. Sloan:
  Corrigendum to Types of Noise in Data for Concept Learning. 450