Structural Minimax Probability Machine
Reads0
Chats0
TLDR
This paper uses two finite mixture models to capture the structural information of the data from binary classification and proposes a structural MPM, which can be interpreted as a large margin classifier and can be transformed to support vector machine and maxi–min margin machine under certain special conditions.Abstract:
Minimax probability machine (MPM) is an interesting discriminative classifier based on generative prior knowledge. It can directly estimate the probabilistic accuracy bound by minimizing the maximum probability of misclassification. The structural information of data is an effective way to represent prior knowledge, and has been found to be vital for designing classifiers in real-world problems. However, MPM only considers the prior probability distribution of each class with a given mean and covariance matrix, which does not efficiently exploit the structural information of data. In this paper, we use two finite mixture models to capture the structural information of the data from binary classification. For each subdistribution in a finite mixture model, only its mean and covariance matrix are assumed to be known. Based on the finite mixture models, we propose a structural MPM (SMPM). SMPM can be solved effectively by a sequence of the second-order cone programming problems. Moreover, we extend a linear model of SMPM to a nonlinear model by exploiting kernelization techniques. We also show that the SMPM can be interpreted as a large margin classifier and can be transformed to support vector machine and maxi–min margin machine under certain special conditions. Experimental results on both synthetic and real-world data sets demonstrate the effectiveness of SMPM.read more
Citations
More filters
Posted ContentDOI
Fuzzy Clustering Optimal k Selection Method Based on Multi-objective Optimization
Wang Lisong,Cui Guonan,Cai Xinye +2 more
TL;DR: Compared with the traditional method, the FMOEA-K can shorten the calculation time and improve the accuracy of calculating the optimal k value, and uses multi-objective optimization algorithm to search the appropriate cluster center concurrently.
Journal ArticleDOI
Segmentation and classification of hyperspectral images using CHV pattern extraction grid
TL;DR: The comparative analysis between the proposed CHV-based pattern extraction grid with the existing techniques regarding the various metrics such as accuracy, sensitivity, specificity, rate variations and coefficient variations assures the effectiveness of proposed work in remote sensing applications.
Journal ArticleDOI
iAI-DSAE: A Computational Method for Adenosine to Inosine Editing Site Prediction
TL;DR: A novel predictor, called iAI-DSAE, was proposed by using three feature extraction methods including din nucleotidebased auto-cross covariance, pseudo dinucleotide composition and nucleotide density methods to identify adenosine-to-inosine RNA editing sites.
Journal ArticleDOI
Evaluating the effect of MIPM on vehicle detection performance
TL;DR: Results indicate that the proposed approach is feasible, and more accurate compared to others, especially when facing bad weather conditions and lighting variations in different environments.
Journal ArticleDOI
Broad Minimax Probability Learning System and its Application in Regression Modeling
TL;DR: Wang et al. as mentioned in this paper proposed a broad minimax probability learning system (BMPLS), which maximizes the worst probability of the regression function being within the allowed error range without making any distributional assumptions of the random error.
References
More filters
Journal ArticleDOI
A tutorial on hidden Markov models and selected applications in speech recognition
TL;DR: In this paper, the authors provide an overview of the basic theory of hidden Markov models (HMMs) as originated by L.E. Baum and T. Petrie (1966) and give practical details on methods of implementation of the theory along with a description of selected applications of HMMs to distinct problems in speech recognition.
Journal ArticleDOI
Hierarchical Grouping to Optimize an Objective Function
TL;DR: In this paper, a procedure for forming hierarchical groups of mutually exclusive subsets, each of which has members that are maximally similar with respect to specified characteristics, is suggested for use in large-scale (n > 100) studies when a precise optimal solution for a specified number of groups is not practical.
Book ChapterDOI
Neural Networks for Pattern Recognition
Suresh Kothari,Heekuck Oh +1 more
TL;DR: The chapter discusses two important directions of research to improve learning algorithms: the dynamic node generation, which is used by the cascade correlation algorithm; and designing learning algorithms where the choice of parameters is not an issue.
Journal ArticleDOI
Using SeDuMi 1.02, a MATLAB toolbox for optimization over symmetric cones
TL;DR: This paper describes how to work with SeDuMi, an add-on for MATLAB, which lets you solve optimization problems with linear, quadratic and semidefiniteness constraints by exploiting sparsity.
Journal ArticleDOI
A comparison of methods for multiclass support vector machines
Hsu Chih-Wei,Chih-Jen Lin +1 more
TL;DR: Decomposition implementations for two "all-together" multiclass SVM methods are given and it is shown that for large problems methods by considering all data at once in general need fewer support vectors.
Related Papers (5)
A Robust Regularization Path Algorithm for $\nu $ -Support Vector Classification
Bin Gu,Victor S. Sheng +1 more