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 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...

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...

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...

Due to myriads of classes, designing accurate and efficient classifiers becomes very challenging for multi-class classification. Recent research has shown that class structure learning can greatly facilitate multi-class learning. In this paper, we propose a novel method to learn the class structure for multi-class classification problems. The class structure is assumed to be a binary hierarchic...