Input space X ⊆ R d. item x ∈ X

We present a reduction framework from ordinal regression to binary classification based on extended examples. The framework consists of three steps: extracting extended examples from the original examples, learning a binary classifier on the extended examples with any binary classification

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We introduce a large-volume box classification for binary prediction, which maintains a subset of weight vectors, and specifically axis-aligned boxes. Our learning algorithm seeks for a box of large volume that contains “simple ” weight vectors which most of are accurate on the training set. Two

Abstract – We introduce a simple and computationally trivial method for binary classification based on the evaluation of potential functions. We demonstrate that despite the conceptual and computational simplicity of the method its performance can match or exceed that of standard Support Vector

Abstract We present a reduction framework from ordinal regression to binary classificationbased on extended examples. The framework consists of three steps: extracting extended examples from the original examples, learning a binary classifier on theextended examples with any binary classification

Abstract We present a reduction framework from ordinal regression to binary classificationbased on extended examples. The framework consists of three steps: extracting extended examples from the original examples, learning a binary classifier on theextended examples with any binary classification

sequentially to classify input variable into several categories. In this paper, these two steps are combined into one resulting in a good classification performance. More specifically, each principal component is treated as a weak classifier in Adaboost algorithm to constitute a strong classifier for binary

This paper proposes a binary classification tree aiming at solving multi-class classification problems using binary classifiers. The tree design is achieved in a way that a class group is partitioned into two distinct subgroups at a node. The node adopts the class-modular scheme to improve

This paper presents a theoretically very simple, yet efficient, multiresolution approach to gray-scale and rotation invariant texture classification based on local binary patterns and nonparametric discrimination of sample and prototype distributions. The method is based on recognizing