The objective of pattern classification is minimizing generalization errors for innumerable unknown samples. In the structural risk minimization (SRM) principle, both empirical errors and complexities of classifiers are minimized instead of minimizing generalization errors. We define a criterion about both of empirical errors and complexities for multiclass classifiers directly, and propose a p...

In this paper, we study the multiclass classification problem. We derive a framework to solve this problem by providing algorithms with the complexity of a single binary classifier. The resulting multiclass machines can be decomposed into two categories. The first category corresponds to vector-output machines, where we develop several algorithms. In the second category, we show that the least-...

This paper describes the contribution of LT3 for the CLPsych 2016 Shared Task on automatic triage of mental health forum posts. Our systems use multiclass Support Vector Machines (SVM), cascaded binary SVMs and ensembles with a rich feature set. The best systems obtain macro-averaged F-scores of 40% on the full task and 80% on the green versus alarming distinction. Multiclass SVMs with all feat...

breast cancer is the second largest cause of cancer deaths among women. at the same time, it is also among the most curable cancer types if it can be diagnosed early. this paper presents a novel hybrid intelligent method for recognition of breast cancer tumors. the proposed method includes three main modules: the feature extraction module, the classifier module and the optimization module. in t...

Identifying relevant features for a classification task is an important issue in machine learning. In this paper, we present a feature crediting scheme for multiclass pattern recognition tasks, that utilizes the ability of Support Vector Machines to generalize well in high dimensional feature spaces. Support Vector learning identifies a small subset of training data relevant for the classificat...

Identifying relevant features for a classification task is an important issue in machine learning. In this paper, we present a feature crediting scheme for multiclass pattern recognition tasks, that utilizes the ability of Support Vector Machines to generalize well in high dimensional feature spaces. Support Vector learning identifies a small subset of training data relevant for the classificat...