This paper presents a new data classification method based on mixed-integer programming. Traditional approaches that are based on partitioning the data sets into two groups perform poorly for multi-class data classification problems. The proposed approach is based on the use of hyper-boxes for defining boundaries of the classes that include all or some of the points in that set. A mixed-integer...

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...

Classification of multi class images is very enviable for different recognition. This is affected by many factors such as noise, blur, low illumination, complex background, occlusion etc. Noise is one of the major factors causing degradation of the classification performance. This paper proposes an efficient method for classification of multi class object images which are corrupted by Gaussian ...

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...

Classifications of several gesture types are very helpful in several applications. This paper tries to address fast classifications of hand gestures using DTW over multi-core simple processors. We presented a methodology to distribute templates over multi-cores and then allow parallel execution of the classification. The results were presented to voting algorithm in which the majority vote was ...

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...