Computational learning theory. Proceedings of the 2nd annual workshop, held at the University of California, Santa Cruz/CA (USA), July 31 – August 2, 1989. (English) Zbl 0741.00077

San Mateo, CA: Morgan Kaufmann Publishers. VI, 389 p. (1989).

Show indexed articles as search result.

The articles of this volume will be reviewed individually.
Indexed articles:
Vapnik, V. N., Inductive principles of the search for empirical dependences. (Methods based on weak convergence of probability measures), 3-21 [Zbl 0746.68075]
Abe, Naoki, Polynomial learnability of semilinear sets, 25-40 [Zbl 0747.68039]
Helmbold, David; Sloan, Robert; Warmuth, Manfred K., Learning nested differences of intersection-closed concept classes, 41-56 [Zbl 0746.68072]
Kearns, Michael; Pitt, Leonard, A polynomial-time algorithm for learning \(k\)-variable pattern languages from examples, 57-71 [Zbl 0747.68050]
Natarajan, B. K., On learning from exercises, 72-87 [Zbl 0747.68056]
Osherson, Daniel N.; Stob, Michael; Weinstein, Scott, On approximate truth, 88-101 [Zbl 0751.03012]
Milosavljević, Aleksandar; Haussler, David; Jurka, Jerzy, Informed parsimonious inference of prototypical genetic sequences, 102-117 [Zbl 0749.68078]
Lin, Jyh-Han; Vitter, Jeffrey Scott, Complexity issues in learning by neural nets, 118-133 [Zbl 0770.68099]
Angluin, Dana, Equivalence queries and approximate fingerprints, 134-145 [Zbl 0760.68058]
Hellerstein, Lisa; Karpinski, Marek, Learning read-once formulas using membership queries, 146-161 [Zbl 0747.68046]
Ishizaka, Hiroki, Learning simple deterministic languages, 162-174 [Zbl 0747.68048]
Fulk, Mark; Jain, Sanjay, Learning in the presence of inaccurate information, 175-188 [Zbl 0747.68042]
Jain, Sanjay; Sharma, Arun; Case, John, Convergence to nearly minimal size grammars by vacillating learning machines. (Extended abstract), 189-199 [Zbl 0747.68049]
Velauthapillai, Mahendran, Inductive inference with bounded number of mind changes, 200-213 [Zbl 0747.68060]
Gasarch, William I.; Pleszkoch, Mark B., Learning via queries to an oracle, 214-229 [Zbl 0747.68043]
Pearl, Judea; Dechter, Rina, Learning structure from data: A survey, 230-244 [Zbl 0746.68074]
Levin, Esther; Tishby, Naftali; Solla, Sara A., A statistical approach to learning and generalization in layered neural networks, 245-260 [Zbl 0770.68098]
Paturi, Ramamohan; Rajasekaran, Sanguthevar; Reif, John, The light bulb problem. (Extended abstract), 261-268 [Zbl 0746.68073]
Littlestone, Nick, From on-line to batch learning, 269-284 [Zbl 0752.68066]
Ben-David, Shai; Benedek, Gyora M.; Mansour, Yishay, A parametrization scheme for classifying models of learnability, 285-302 [Zbl 0746.68070]
Kurtz, Stuart A.; Smith, Carl H., On the role of search for learning, 303-311 [Zbl 0745.68086]
Arikawa, Setsuo; Shinohara, Takeshi; Yamamoto, Akihiro, Elementary formal system as a unifying framework for language learning, 312-327 [Zbl 0747.68040]
Wright, Keith, Identification of unions of languages drawn from an identifiable class, 328-333 [Zbl 0747.68061]
Kelly, Kevin T., Induction from the general to the more general, 334-348 [Zbl 0747.68051]
Floyd, Sally, Space-bounded learning and the Vapnik-Chervonenkis dimension, 349-364 [Zbl 0747.68041]
Kivinen, Jyrki, Reliable and useful learning, 365-380 [Zbl 0747.68052]
Schapire, Robert E., The strength of weak learnability, 383 [Zbl 0747.68058]
Maass, Wolfgang; Turán, György, On the complexity of learning from counterexamples, 384 [Zbl 0747.68055]
Haussler, David, Generalizing the PAC model: Sample size bounds from metric dimension- based uniform convergence results, 385 [Zbl 0747.68045]
Li, Ming; Vitanyi, Paul M. B., A theory of learning simple concepts under simple distributions, 386 [Zbl 0747.68053]
Goldman, Sally A.; Rivest, Ronald L.; Schapire, Robert E., Learning binary relations and total orders. (Abstract), 387 [Zbl 0747.68044]
Littlestone, Nick; Warmuth, Manfred K., The weighted majority algorithm, 388 [Zbl 0747.68054]


00B25 Proceedings of conferences of miscellaneous specific interest
68-06 Proceedings, conferences, collections, etc. pertaining to computer science