Computing Reviews
Today's Issue Hot Topics Search Browse Recommended My Account Log In
Browse by topic Browse by titles Authors Reviewers Browse by issue Browse Help
Search
  Browse All Reviews > Computing Methodologies (I) > Artificial Intelligence (I.2) > Learning (I.2.6) > Concept Learning (I.2.6...)  
 
Options:
 
  1-10 of 21 Reviews about "Concept Learning (I.2.6...)": Date Reviewed
  On equivalence of conceptual scaling and generalized one-sided concept lattices
Butka P., Pócs J., Pócsová J. Information Sciences 25957-70, 2014.  Type: Article

A technical account with mathematical proofs, this paper shows that the methods of conceptual scaling and generalized one-sided concept lattices are equivalent....

Jun 3 2014
  A reinforcement learning algorithm based on policy iteration for average reward: empirical results with yield management and convergence analysis
Gosavi A. Machine Learning 55(1): 5-29, 2004.  Type: Article

An algorithm for solving average reward Markov and semi-Markov decision problems is presented in this paper. The algorithm is asynchronous and model free, and is capable of cooperating with a nearest neighbor approach to manage large s...

Jul 28 2005
  Incremental learning with partial instance memory
Maloof M., Michalski R. Artificial Intelligence 154(1-2): 95-126, 2004.  Type: Article

The primary problem with online incremental machine learning methods is the tradeoff between predictive accuracy and the increasing computational costs of keeping increasing amounts of training data....

Nov 3 2004
  Queries revisited
Angluin D. Theoretical Computer Science 313(2): 175-194, 2004.  Type: Article

A concept is a subset of a finite domain. We want to bind the number of questions that have to be asked in order to identify a concept that comes from a known concept class. These bounds depend on which kinds of questions can be asked....

May 19 2004
  Online learning in online auctions
Blum A., Kumar V., Rudra A., Wu F.  Discrete algorithms (Proceedings of the fourteenth annual ACM-SIAM symposium, Baltimore, Maryland, Jan 12-14, 2003) 202-204, 2003.  Type: Proceedings

Performance of an online auction can be improved by an algorithm that learns from the set of bids already made. An online auction receives bids, and deals with each individually, deciding whether to accept a bid or wait for a higher on...

Dec 31 2003
  Unsupervised Learning of Human Motion
Song Y., Goncalves L., Perona P. IEEE Transactions on Pattern Analysis and Machine Intelligence 25(7): 814-827, 2003.  Type: Article

A method for learning a probabilistic model of human motion, in an unsupervised fashion, from unlabeled cluttered data is presented. The models obtained are mixtures of Gaussian models and conditional independence, described by a decom...

Dec 11 2003
  Support vector machine active learning with applications to text classification
Tong S., Koller D. The Journal of Machine Learning Research 2(1): 45-66, 2001.  Type: Article

This well-written paper is a major contribution to the field of active learning by support vector machines (SVMs). Usually, machine learning methods are passive: machines learn from a set of labeled training data. In active learning, h...

Nov 4 2003
  A bio-inspired robotic mechanism for autonomous locomotion in unconventional environments
Maravall D., de Lope J. In Autonomous robotic systems. Heidelberg, Germany: Physica-Verlag GmbH, 2003.  Type: Book Chapter

Maravall and de Lope describe a robotic model capable of navigating along aerial power, telephone, or railroad lines, as well as in reticulated structures....

Sep 17 2003
  Scale-sensitive dimensions, uniform convergence, and learnability
Alon N., Ben-David S., Cesa-Bianchi N., Haussler D. Journal of the ACM 44(4): 615-631, 1997.  Type: Article

Inspired by Valiant’s PAC learning model, the authors, using discretization techniques, seek a convergence of distribution-free expectations over classes of random variables. Real-valued functions (such as Glivenko-Cantelli c...

Oct 1 1998
  Learnable classes of categorial grammars
Kanazawa M., Cambridge University Press, New York, NY, 1998.  Type: Book (9781575860978)

The focus of this monograph is on learning categorial grammars from sequences of positive examples presented in the form of functor-argument structures. Sakakibara [1] has demonstrated that the class of reversible context-free grammars...

Jul 1 1998
 
 
 
Display per page
Send Your Comments
Contact Us
Reproduction in whole or in part without permission is prohibited.   Copyright 1999-2024 ThinkLoud®
Terms of Use
| Privacy Policy