General Algorithmic Frameworks for Online Problem.

Yair Censor, Simeon Reich, Alexander J Zaslavski
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Abstract

We study general algorithmic frameworks for online learning tasks. These include binary classification, regression, multiclass problems and cost-sensitive multiclass classification. The theorems that we present give loss bounds on the behavior of our algorithms which depend on general conditions on the iterative step sizes.

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在线问题的一般算法框架。
我们研究在线学习任务的一般算法框架。其中包括二元分类、回归、多类问题和代价敏感多类分类。我们提出的定理给出了我们的算法行为的损失界,它依赖于迭代步长的一般条件。
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TWO AUTOCOVARIANCE-BASED MEASURES OF BALANCE IN PARKINSONIANS AND NORMAL CONTROLS. General Algorithmic Frameworks for Online Problem. ELECTRONIC PLATFORM MEASURES OF BALANCE IMPAIRMENT IN PARKINSONIANS AND FIRST DEGREE RELATIVES.
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