3 edition of Proceedings of the Sixth Annual ACM Conference on Computational Learning Theory found in the catalog.
|Other titles||Proceedings of the 6th Annual ACM Conference on Computational Learning Theory., COLT "93.|
|Statement||sponsored by ACM SIGACT and ACM SIGART.|
|Contributions||ACM Special Interest Group for Automata and Computability Theory., SIGART.|
|LC Classifications||Q325.5 .A26 1993|
|The Physical Object|
|Pagination||vi, 463 p. :|
|Number of Pages||463|
|LC Control Number||95139263|
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Madhu Sudan: Papers - Bibliographic Information. Online algorithms for locating checkpoints. Authors: Marshall Bern, Daniel H. Greene, Arvind Raghunathan, and Madhu Sudan. Proceedings of the Twenty Second Annual ACM Symposium on Theory of Computing, pages , Baltimore, Maryland, May (). Algorithmica, 11(1): , January note = "Proceedings of the Fifth Annual ACM Workshop on Computational Learning Theory ; Conference date: Through ", Kearns, MJ, Schapire, RE & Sellie, LM , Toward efficient agnostic learning. in Proceedings of the Fifth Annual ACM Workshop on Computational Learning ://
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Freivalds R, Kinber E and Smith C On the impact of forgetting on learning machines Proceedings of the sixth annual conference on Computational learning theory, () Angluin D Computational learning theory Proceedings of the twenty-fourth annual ACM symposium on Theory of Computing, () Fixedparameter intractability II.
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Proceedings of the Seventh Annual ACM Conference on Computational Learning Theory. New York, N.Y.: Association for Computer Machinery, © (OCoLC) Material Type: Conference publication, Internet resource: Document Type: Book, Internet Resource Proceedings of the Sixth Annual ACM Conference on Computational Learning Theory, July 26thth,Santa Cruz, California sponsored by ACM SIGACT and ACM SIGART The Association for Computing Machinery, Proceedings of the Twelfth Annual ACM-SIAM Symposium on Discrete Algorithms (), S.
Rao Kosaraju, editor Proceedings of the Tenth SIAM Conference on Parallel Processing for Scientific Computing (), Charles Koelbel and Juan Meza, editors Computational Information Retrieval (), Michael Berry, editor The ACM Symposium on Theory of Computing (STOC) includes topics of interest such as: algorithms and data structures, computational complexity, cryptography, privacy, computational geometry, algorithmic graph theory and combinatorics, optimization, randomness in computing, approximation algorithms, parallel and distributed computation, machine learning, applications of logic, algorithmic Carlo Strapparava and Rada Mihalcea, Learning to Identify Emotions in Text, in Proceedings of the ACM Conference on Applied Computing ACM-SACFortaleza, Brazile, March [ pdf ] Rada Mihalcea, Review of "The Text Mining Handbook: Advanced Approaches to Analyzing Unstructured Data" by Ronen Feldman and James Sanger, Computational ~mihalcea/ ACM Digital Library Home page.
The ACM Special Interest Group on Algorithms and Computation Theory is an international organization that fosters and promotes the discovery and dissemination of high quality research in theoretical computer science (TCS), the formal analysis of efficient computation and computational :// ACM Conference on Computer and Communications Security A+ CHI International Conference on Human Factors in Computing Systems A+ COLT Annual Conference on Computational Learning Theory A+ CRYPTO Advances in Cryptology A+ CSCL A+ DCC Proceedings of the Seventh Annual ACM Conference on Computational Learning Theory (COLT ), July, New Brunswick, NJ, USA.
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Quan Wang, Zheng Cao, Jun Xu, and Hang Li. Group Matrix Factorization for Scalable Topic ~junxu. Downey, P. Evans, and M. Fellows. Parameterized learning Complexity. Proceedings of the Sixth Annual Conference on Computational Learning Theory, (COLT’93)IEEE, () 51 – On the complexity of function learning,Proceedings of the Sixth Annual ACM Conference on Computational Learning Theory, pp.
– Google Scholar. Angluin, D. Queries and concept learning,Machine Learning, 2(4)– Google Scholar. Barland, I. Some ideas on learning with directional :// In Proceedings of the Twenty-Sixth International Conference on Machine Learning, A majorization-minimization algorithm for (multiple) hyperparameter learning, Chuan Sheng Foo, Chuong Do and Andrew Y.
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leading the development of various datacenter management solutions with his current interests in machine learning and data-driven approaches to system modeling, management, and optimization