We consider the problem of multi-class classification and a stochastic optimization approach to it. The idea is to, instead of weighing classes, make use of the total sum of margins as a regularization. As general the problem is hard to solve, we use Bregman divergence as the regularizer and end up with a proximal mirror descent with a specific distance-generating function. The approach is desi...

1. Mathematical model. 1.1. Multi objective integer programming model. A kmesindeki her a1, a2, . . . , am noktas iin kar gelen KF’ler srasyla g1, g2, . . . , gm olsun. Bu fonksiyonlarn elde edilmesinin ardndan, her fonksiyonun hangi noktalar ayrdn gsteren bir Pm×m matrisi, eer A kmesindeki i. nokta, ai, l. fonksiyon ile ayrlyor ise Pil = 1, dier durumda Pil = 0 olacak ekilde oluturulsun. Bu aa...

Automatic text categorization has become a vital topic in many applications. Imagine for example the automatic classification of Internet pages for a search engine database. The traditional 1-of-n output coding for classification scheme needs resources increasing linearly with the number of classes. A different solution uses an error correcting code, increasing in length with O(log2(n)) only. I...

Animacy is the semantic property of nouns denoting whether an entity can act, or is perceived as acting, of its own will. This property is marked grammatically in various languages, albeit rarely in English. It has recently been highlighted as a relevant property for NLP applications such as parsing and anaphora resolution. In order for animacy to be used in conjunction with other semantic feat...

In this paper, a multi-class feature selection scheme based on recursive feature elimination (RFE) is proposed for texture classifications. The feature selection scheme is performed in the context of one-against-all least squares support vector machine classifiers (LSSVM). The margin difference between binary classifiers with and without an associated feature is used to characterize the discrim...

Many applications require the ability to identify data that is anomalous with respect to a target group of observations, in the sense of belonging to a new, previously unseen ‘attacker’ class. One possible approach to this kind of verification problem is one-class classification, learning a description of the target class concerned based solely on data from this class. However, if known non-tar...

This paper presents a Tikhonov regularization based piecewise classification model for multi-category discrimination of sets or objects. The proposed model includes a linear classification and nonlinear kernel classification model formulation. Advantages of the regularized multi-classification formulations include its ability to express a multi-class problem as a single and unconstrained optimi...

In this paper, a high-speed online neural network classifier based on extreme learning machines for multi-label classification is proposed. In multi-label classification, each of the input data sample belongs to one or more than one of the target labels. The traditional binary and multi-class classification where each sample belongs to only one target class forms the subset of multi-label class...

SVMs were initially developed to perform binary classification; though, applications of binary classification are very limited. Most of the practical applications involve multiclass classification, especially in remote sensing land cover classification. A number of methods have been proposed to implement SVMs to produce multiclass classification. A number of methods to generate multiclass SVMs ...

Multi-class classification is one of the fundamental tasks in bioinformatics and typically arises in cancer diagnosis studies by gene expression profiling. This article reviews two recent approaches to multi-class classification by combining multiple binary classifiers, which are formulated based on a unified framework of error-correcting output coding (ECOC). The first approach is to construct...