On polynomial-time probably almost discriminative learnability
COLT '93 Proceedings of the sixth annual conference on Computational learning theory
General bounds on the number of examples needed for learning probabilistic concepts
COLT '93 Proceedings of the sixth annual conference on Computational learning theory
The minimum L-complexity algorithm and its applications to learning non-parametric rules
COLT '94 Proceedings of the seventh annual conference on Computational learning theory
Efficient distribution-free learning of probabilistic concepts
Journal of Computer and System Sciences - Special issue: 31st IEEE conference on foundations of computer science, Oct. 22–24, 1990
COLT '95 Proceedings of the eighth annual conference on Computational learning theory
A randomized approximation of the MDL for stochastic models with hidden variables
COLT '96 Proceedings of the ninth annual conference on Computational learning theory
COLT '96 Proceedings of the ninth annual conference on Computational learning theory
Machine Learning - Special issue on the ninth annual conference on computational theory (COLT '96)
Text classification using ESC-based stochastic decision lists
Proceedings of the eighth international conference on Information and knowledge management
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Mining from open answers in questionnaire data
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Open Answers in Questionnaire Data
IEEE Intelligent Systems
Text classification using ESC-based stochastic decision lists
Information Processing and Management: an International Journal
Mining product reputations on the Web
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Learning from Cluster Examples
Machine Learning
Generalizing case frames using a thesaurus and the MDL principle
Computational Linguistics
Word clustering and disambiguation based on co-occurrence data
Natural Language Engineering
Clustering words with the MDL principle
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
Mining traffic data from probe-car system for travel time prediction
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Real-Valued LCS Using UNDX for Technology Extraction
KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part II
Technology extraction from time series data reflecting expert operator skills and knowledge
International Journal of Computer Applications in Technology
Technology Extraction of Expert Operator Skills from Process Time Series Data
Learning Classifier Systems
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Linear Time Model Selection for Mixture of Heterogeneous Components
ACML '09 Proceedings of the 1st Asian Conference on Machine Learning: Advances in Machine Learning
| Hi-index | 0.00 |
This paper proposes a learning criterion for stochastic rules. This criterion is developed by extending Valiant's PAC (Probably Approximately Correct) learning model, which is a learning criterion for deterministic rules. Stochastic rules here refer to those which probabilistically asign a number of classes, {Y}, to each attribute vector X. The proposed criterion is based on the idea that learning stochastic rules may be regarded as probably approximately correct identification of conditional probability distributions over classes for given input attribute vectors. An algorithm (an MDL algorithm) based on the MDL (Minimum Description Length) principle is used for learning stochastic rules. Specifically, for stochastic rules with finite partitioning (each of which is specified by a finite number of disjoint cells of the domain and a probability parameter vector associated with them), this paper derives target-dependent upper bounds and worst-case upper bounds on the sample size required by the MDL algorithm to learn stochastic rules with given accuracy and confidence. Based on these sample size bounds, this paper proves polynomial-sample-size learnability of stochastic decision lists (which are newly proposed in this paper as a stochastic analogue of Rivest's decision lists) with at most k literals (k is fixed) in each decision, and polynomial-sample-size learnability of stochastic decision trees (a stochastic analogue of decision trees) with at most k depth. Sufficient conditions for polynomial-sample-size learnability and polynomial-time learnability of any classes of stochastic rules with finite partitioning are also derived.