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Support vector machine (SVM) is a popular machine learning method and has been widely applied in many real-world applications. Since SVM is sensitive to noises, fuzzy SVM (FSVM) has been proposed to relieve the over-fitting problem caused by noises through assigning a fuzzy membership to each sample. Then, different samples make different contributions to the learning of classification hyperplane...
The paper proposes a classification model for human behavioral patterns recognition in which the decisions are provided based on several Support Vector Machines classifiers within a multi-level decision structure. SVMs are suitable for applications in which the input data feature spaces are very large, involving many features. The human behavior recognition is a relevant example of such application...
Predicting the locations of Response Elements (RE) has received considerable attention in the field of gene sequence analysis and bioinformatics. Protein53 (p53) has a prominent role in the cell cycle and cancer prevention; it functions as a transcription factor and binds with p53 REs in the DNA. The identification of p53 response elements enlightens the unknown functions and characteristics of p53...
Machine learning techniques have been actively pursued in the last years, mainly due to the great number of applications that make use of some sort of intelligent mechanism for decision-making processes. In this context, we shall highlight pruning strategies, which provide heuristics to select from a collection of classifiers the ones that can really improve recognition rates when working together...
In this paper, we discuss a novel approach to incrementally construct a rule ensemble. The approach constructs an ensemble from a dynamically generated set of rule classifiers. Each classifier in this set is trained by using a different class ordering. We investigate criteria including accuracy, ensemble size, and the role of starting point in the search. Fusion is done by averaging. Using 22 data...
Classifier competence is critical important for dynamic classifier selection. This study proposes a semi-supervised learning algorithm to learn the competence of classifiers under the proposed optimization framework based on graph. First it constructs a graph based on the training data and some unlabeled data. Then it iteratively learns the competence of classifiers. The learned competence not just...
This paper explores the supervised pattern recognition problem based on feature partitioning. This formulation leads to a new problem in computational geometry. The supervised pattern recognition problem is formulated as an heuristic good clique cover problem satisfying the k-nearest neighbors rule. First it is applied a heuristic algorithm for partitioning a graph into a minimal number of cliques...
Support Vector Machine (SVM) is an algorithm that trains and classifies different types of data through of an optimal hyperplane of decision. On the other hand, Particle Swarm Optimization (PSO) is, in general, an algorithm that finds the best point to represent a dataset. In this paper, PSO is used to find the best data of each class (pattern) to be trained by SVM and there is a comparison of the...
Pattern recognition (PR) based myoelectric hand control has become a research focus in the field of rehabilitative engineer and intelligent control. However, the state of the art method is hardly adopted for clinical use because of signal interfered by shift, fatigue and user-unfriendly of retraining. The aim of this study is to evaluate the performance of different kinds of online algorithms in classifying...
The paper proposes an innovative supervised learning method for human behavioral recognition in which the behavioral patterns are classified according to the classes importance. A detector classifier is trained to recognize the human behavioral patterns belonging to the most important class. The optimization is performed by fixing the classifier operating point to provide the appropriate performance...
Learning from large data sets that contain samples of unknown or incorrect labels becomes increasingly important. Such problems are inherent to many big data scenarios, hence there is a need for developing robust generic approaches to learning from difficult data. In this paper, we propose a new memetic algorithm that evolves samples and labels to select a training set for support vector machines...
In this paper, we propose a multi-kernel classifier learning algorithm to optimize a given nonlinear and nonsmoonth multivariate classifier performance measure. Moreover, to solve the problem of kernel function selection and kernel parameter tuning, we proposed to construct an optimal kernel by weighted linear combination of some candidate kernels. The learning of the classifier parameter and the...
In this work, a new training algorithm for probabilistic neural networks (PNN) is presented. The proposed algorithm addresses one of the major drawbacks of probabilistic neural networks, which is the size of the hidden layer in the network. By using a cross-validation training algorithm, the number of hidden neurons is shrunk to a smaller number consisting of the most representative samples of the...
In this paper, we propose a robust proximal classifier via absolute value inequalities (AVIPC) for pattern classification. AVIPC determines K proximal planes by solving K optimization problems with absolute value inequalities. In AVIPC, each proximal plane is closer to one class and far away from the others. By using the absolute value inequalities, AVIPC is more robust and sparse than traditional...
Natural language dialogue is an important component of interaction between ordinary users and complex computer applications. Short Text Semantic Similarity algorithms have been developed to improve the efficiency of producing sophisticated dialogue systems. Such algorithms are currently unable to discriminate between different dialogue acts (assertions, questions, instructions etc.), requiring the...
Pattern recognition problems occur in many fields and hence effective classification algorithms are the focus of much research. In various circumstances not classification accuracy but misclassification cost minimsation is the primary goal leading to the development of cost-sensitive classification algorithms. In this paper, we show how evolutionary algorithms, in particular genetic algorithms (GAs),...
With changes in insulated defects, the environment, and so on, new partial discharge (PD) data are highly different from the original samples. It leads to a decrease in on-line recognition rate. Using ultra-high frequency (UHF) cumulative energy and its corresponding apparent discharge as inputs, a support vector machine (SVM) incremental method based on simulated annealing (SA) is constructed. Examples...
This paper explores the classification problem based on parallel feature partitioning. This formulation leads to a new problem in computational geometry. While this new problem appears to be NP-complete, it is shown that the proposed graph theoretical platform makes it semi-tractable, allowing the use of conventional tools for its solution. Here, by conventional, we mean any exact or heuristic algorithm...
The mathematical modeling of classifier has been intensively investigated in pattern recognition for decades. Maximin classifier, which conducts optimization based on the perpendicularly closest data point(s) to the decision boundary, has been widely used. However, such method may lead to inferior performance when the boundary data point(s) is significantly influenced by noise. This paper presents...
Based on the consideration that multiset integrated canonical correlation analysis (MICCA) does not include the class information of the samples, this paper presents a discriminative learning version of MICCA, called discriminative-analysis of multiset integrated canonical correlations (DMICC). The extracted features by DMICC not only contain the class information of training samples, but also possess...
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