We prove a quantitative connection between the expected sum of rewards of a policy and binary classification performance on created subproblems. This connection holds without any ...
Imagine two identical people receive exactly the same training on how to classify certain objects. Perhaps surprisingly, we show that one can then manipulate them into classifying...
Xiaojin Zhu, Bryan R. Gibson, Kwang-Sung Jun, Timo...
The article describes a method combining two widely-used empirical approaches to learning from examples: rule induction and instance-based learning. In our algorithm (RIONA) decisi...
A general classification framework, called boosting chain, is proposed for learning boosting cascade. In this framework, a "chain" structure is introduced to integrate h...
In this paper, a novel method of relevance feedback is presented based on Support Vector Machine learning in the content-based image retrieval system. A SVM classifier can be lear...