Marginal maximum entropy partitioning yields asymptotically consistent probability density functions

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volumn 23, Issue 4, 2001, Pages 414-417

  Chau, Tom ^a,b  

^a IEEE   (Canada)

^b UNIVERSITY OF TORONTO   (Canada)

Author keywords

Asymptotic optimal classification; Marginal maximum entropy; Pattern discovery; Recursive partitioning

Indexed keywords

ASYMPTOTIC OPTIMAL CLASSIFICATION; MARGINAL MAXIMUM ENTROPY; PATTERN DISCOVERY; RECURSIVE PARTITIONING;

ALGORITHMS; ASYMPTOTIC STABILITY; DATA STRUCTURES; OPTIMIZATION; PROBABILITY DENSITY FUNCTION; RECURSIVE FUNCTIONS;

PATTERN RECOGNITION;

EID: 0035306422     PISSN: 01628828     EISSN: None     Source Type: Journal    
DOI: 10.1109/34.917576     Document Type: Article

References (21)

1. Solving data acquisition and processing problems in power systems using a pattern analysis approach
2. Information synthesis based on hierarchical maximum entropy discretization
3. Information discovery through hierarchical maximum entropy discretization
4. Pattern discovery by residual analysis and recursive partitioning
5. A nonparametric partitioning procedure for pattern classification
6. Asymptotically efficient solutions to the classification problem
7. A partitioning algorithm with application in pattern classification and the optimization of decision trees
8. A recursive partitioning decision rule for nonparametric classification
9. Some nonparametric multivariate procedures based on statistically equivalent blocks
10. Design of hierarchical classifiers
11. Automatic construction of decision trees from data: A multi-disciplinary survey
12. On maximum entropy discretization and its applications in pattern recognition
13. Mathematical theory of communication
14. Information theory and statistical mechanics I