Classification

Shinto Eguchi () and Osamu Komori ()
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Shinto Eguchi: Institute of Statistical Mathematic
Osamu Komori: Seikei University

Chapter Chapter 7 in Minimum Divergence Methods in Statistical Machine Learning, 2022, pp 179-195 from Springer

Abstract: Abstract This chapter discusses recent developments for pattern recognitionPattern recognitionfocusing on boosting approach in machine learning. The statistical properties such as Bayes risk for several loss functions are discussed in a probabilistic framework. There are a number of loss functions proposed for different purposes and targets. A unified derivation is given by a generator function U which naturally defines entropy, divergence and loss function. The class of U-loss functionsU-loss function associates with the boosting learning algorithms for the loss minimization, which includes AdaBoostAdaBoost and LogitBoost as a twin generated from Kullback-Leibler divergence, and the (partial) area under the ROCReceiver Operating Characteristic (ROC) curve curve(Partial) area under the ROC curve, the.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-4-431-56922-0_7

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DOI: 10.1007/978-4-431-56922-0_7

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