We develop abc-logitboost, based on the prior work on abc-boost[10] and robust logitboost[11]. Our extensive experiments on a variety of datasets demonstrate the considerable improvement of abc-logitboost over logitboost and abc-mart.

The paper suggests the on-line multi-class classi er with a sublinear computational complexity relative to the number of training objects. The proposed approach is based on the combining of two-class probabilistic classi ers. Pairwise coupling is a popular multi-class classication method that combines all comparisons for each pair of classes. Unfortunately pairwise coupling su ers in many cases...

In this article, we study rates of convergence of the generalization error of multi-class margin classifiers. In particular, we develop an upper bound theory quantifying the generalization error of various large margin classifiers. The theory permits a treatment of general margin losses, convex or nonconvex, in presence or absence of a dominating class. Three main results are established. First...

A b s t r a c t : Support vector machines (SVMs) are primarily designed for 2-class classification problems. Although in several papers it is mentioned that the combination of K SVMs can be used to solve a K-class classification problem, such a procedure requires some care. In this paper, the scaling problem of different SVMs is highlighted. Various normalization methods are proposed to cope wi...

This paper presents a new study on a method of designing a multi-class classifier: Data-driven Error Correcting Output Coding (DECOC). DECOC is based on the principle of Error Correcting Output Coding (ECOC), which uses a code matrix to decompose a multi-class problem into multiple binary problems. ECOC for multi-class classification hinges on the design of the code matrix. We propose to explor...

We consider the problem of multi-class classification and a stochastic optimization approach to it. We derive risk bounds for stochastic mirror descent algorithm and provide examples of set geometries that make the use of the algorithm efficient in terms of error in k.

Abc-boost is a new line of boosting algorithms for multi-class classification, by utilizing the commonly used sum-to-zero constraint. To implement abc-boost, a base class must be identified at each boosting step. Prior studies used a very expensive procedure based on exhaustive search for determining the base class at each boosting step. Good testing performance of abc-boost (implemented as abc...

The term “big data” is now becoming more and more important in many fields. This data should not only be gathered, but also analyzed and, in some cases, classified. The categorization of each sample is becoming increasingly multifaceted, since it often means to assign not only one category from one ontology but multiple labels from multiple ontologies. This study investigates the improvement of...

Predicting a protein’s structural class from its amino acid sequence is a fundamental problem in computational biology. Recent machine learning work in this domain has focused on developing new input space representations for protein sequences, that is, string kernels, some of which give state-of-the-art performance for the binary prediction task of discriminating between one class and all the ...

We present a novel multi-output Gaussian process model for multi-class classification. We build on the formulation of Gaussian processes via convolution of white Gaussian noise processes with a parameterized kernel and present a new class of multi-output covariance functions. The latter allow for greater flexibility in modelling relationships between outputs while being parsimonious with regard...