NDLI: A multiresolution approach to pattern recognition

Content Provider	IEEE Xplore Digital Library
Author	Scott, C. Nowak, R.
Copyright Year	2003
Description	Author affiliation: Electr. & Comput. Eng., Rice Univ., Houston, TX, USA (Scott, C.)
Abstract	This paper reports on a family of computationally practical classifiers called dyadic classification trees (DCTs). Like many multiresolution methods in other application areas, DCTs are formed by recursive dyadic partitioning of the input space, followed by pruning to avoid overfitting. We investigate three pruning rules, each motivated by statistical learning theory. These pruning rules involve penalties that are non-additive, data-dependent, and scale-dependent. They produce learning rules that achieve near-minimax rates of convergence for a certain class of problems defined in terms of the smoothness of the Bayes decision boundary. Efficient algorithms exist for implementing each pruning rule. We then briefly mention an extension of dyadic classification trees that places polynomial decision boundaries at each leaf node. This polynomial decorated dyadic classification trees achieve faster rates for smoother decision boundaries, and have improved approximation capabilities relative to classifiers that employ a single polynomial decision rule, such as polynomial-kernel SVMs.
Sponsorship	IEEE Signal Processing Soc
Starting Page	417
Ending Page	420
File Size	310065
Page Count	4
File Format	PDF
ISBN	0780379977
DOI	10.1109/SSP.2003.1289435
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-09-28
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Pattern recognition Polynomials Signal resolution Classification tree analysis Convergence Discrete cosine transforms Application software Image resolution Data analysis Signal processing
Content Type	Text
Resource Type	Article

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