As a new general machine-learning tool based on structural risk minimization principle, Support Vector Machines (SVM) has the advantageous characteristic of good generalization. For this reason, the application of SVM in fault diagnosis field has becomes one growing reach focus. In this paper, data fusion strategy based on multi-class SVMs is proposed to diagnose the gear fault. The fault featu...

In this paper, we consider a pattern generation method for multi-class classification using logical analysis of data (LAD). Specifically, we apply two decomposition approaches—one versus all, and one versus one— to multi-class classification problems, and develop an efficient iterative genetic algorithm with flexible chromosomes and multiple populations (IGA-FCMP). The suggested algorithm has t...

Correlation filters have attracted growing attention due to their high efficiency, which have been well studied for binary classification. However, by setting the desired output to be a fixed Gaussian function, the conventional multi-class classification based on correlation filters becomes problematic due to the under-fitting in many real-world applications. In this paper, we propose an adapti...

In this paper, we present a probabilistic method of dealing with multiclass classification using Stochastic Logic Programs (SLPs), a Probabilistic Inductive Logic Programming (PILP) framework that integrates probability, logic representation and learning. Multi-class prediction attempts to classify an observed datum or example into its proper classification given that it has been tested to have...

We present a multi-layer group sparse coding framework for concurrent image classification and annotation. By leveraging the dependency between image class label and tags, we introduce a multi-layer group sparse structure of the reconstruction coefficients. Such structure fully encodes the mutual dependency between the class label, which describes the image content as a whole, and tags, which d...

In this work we discuss a regularization framework to solve multi-category classification when the classes are described by an underlying class taxonomy. In particular we discuss how to learn the class taxonomy while learning a multi-category classifier.

The method of common spatio-spectral patterns (CSSPs) is an extension of common spatial patterns (CSPs) by utilizing the technique of delay embedding to alleviate the adverse effects of noises and artifacts on the electroencephalogram (EEG) classification. Although the CSSPs method has shown to be more powerful than the CSPs method in the EEG classification, this method is only suitable for two...

The coding matrix design plays a fundamental role in the prediction performance of the error correcting output codes (ECOC)-based multi-class task. In many-class classification problems, e.g., fine-grained categorization, it is difficult to distinguish subtle between-class differences under existing coding schemes due to a limited choices of coding values. In this paper, we investigate whether ...

In recent years, indoor scene recognition has attracted much attention and its research has rapidly expanded by not only engineers but also neuroscientists, since it has many potential applications in computer vision communication and automatic access control system. Especially, indoor scene recognition is an important part of computer vision and feature recognition as the first step of automat...