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The problem of fusing indefinite similarity information and positive semidefinite similarity information together for classification is considered. The proposed solution jointly (i) learns a spectrum modification to make the indefinite similarity positive semidefinite, (ii) learns a conic combination of multiple given positive semidefinite kernels, and (iii) learns the parameters of a discriminative...
GTM neurolike structures provide solutions of such tasks as: pattern recognition, prediction tasks, classification, Principal Components Analysis, factor analysis, optimization, lost data renewal or its (data) compression, realization of information security methods, solution of algebraic equations systems (including underdetermined and overdetermined), high dimensional data visualization, etc.
In order to recognize stratums, a new support vector machine model (SVMM) is built on the basis of well-logging data and with RBF as its kernel function. Through the optimization of penalty parameter C and the introduction of a discriminant function, the classification accuracy of SVMM is greatly enhanced. Experiments show that the SVM classifier can be applied effectively to the recognition of stratums,...
AdaBoost was proposed as an efficient algorithm of the ensemble learning field, it selects a set of weak classifiers and combines them into a final strong classifier. However, conventional AdaBoost is a sequential forward search procedure using the greedy selection strategy, redundancy can not be avoided. We proposed a post optimization procedure for the found classifiers and their coefficients based...
The incorporation of prior knowledge into SVMs for classification is the key element that allows increasing the performance to many applications. Wu proposed weighted margin support vector machine (WMSVM), the scalability aspect of the approach to handle large data sets still needs much of exploration. In this paper, we describe a generalization of weighted margin multi-class core vector machine (WMMCVM)...
Social insects like ants, bees deposit pheromone (a type of chemical) in order to communicate between the members of their community. Pheromone, that causes clumping behavior in a species and brings individuals into a closer proximity, is called aggregation pheromone. This article presents a new algorithm (called, APC) for pattern classification based on the property of aggregation pheromone found...
This paper presents a new implementable algorithm for solving the Lipschitz classifier that is a generalization of the maximum margin concept from Hilbert to Banach spaces. In contrast to the support vector machine approach, our algorithm is free to use any finite family of continuously differentiable functions which linearly compose the decision function. Nevertheless, robustness properties are maintained...
Pairwise coupling is a widely used method in multi-class SVM and max wins voting (MWV) strategy can obtain a global classification by considering each partial answer of binary classifier as vote. But MWV strategy has an important drawback, due to the nonsense caused by those meaningless binary classifier. This paper presents a novel approach, which considers the pairwise SVM classification as a decision-making...
Class imbalance is a ubiquitous problem in supervised learning and has gained wide-scale attention in the literature. Perhaps the most prevalent solution is to apply sampling to training data in order improve classifier performance. The typical approach will apply uniform levels of sampling globally. However, we believe that data is typically multi-modal, which suggests sampling should be treated...
One of disadvantages of Hidden Markov Models (HMMs) is its low resistance to unexpected noises among observation sequences. Unexpected noises in a sequence usually ??break?? a sequence of observations, and then makes this sequence unrecognizable for trained models. We propose a new HMM training and testing scheme, which compensates some of the negative effects of such noises. We carried out experiment...
When applying traditional methods to train approximately linear support vector machine (SVM), we will get a kernel matrix which occupy mass computer memory and lead a slow convergence speed. In order to improve the convergence speed of SVM, a method of training approximately linear support vector machine based on variational inequality (VIALSVM) was proposed. The method turns the convex quadratic...
We present a distributed machine learning framework based on support vector machines that allows classification problems to be solved iteratively through parallel update algorithms with minimal communication overhead. Decomposing the main problem into multiple relaxed subproblems allows them to be simultaneously solved by individual computing units operating in parallel and having access to only a...
With the increase of the training set??s size, the efficiency of support vector machine (SVM) classifier will be confined. To solve such a problem, a novel pre-extracting method for SVM classification is proposed in this paper. In SVM classification, only support vectors (SVs) have significant influence on the optimization result. We adopt a non-parametric k-NN rule called relative neighborhood graph...
In this study, we propose a novel optimization algorithm for minimum classification error (MCE) training of modified quadratic discriminant function (MQDF) models. An ellipsoid constrained quadratic programming (ECQP) problem is formulated with an efficient line search solution derived, and a subspace combination condition is proposed to simplify the problem in certain cases. We show that under the...
Structural design of an artificial neural network is a very important phase in the construction of such a network. The selection of the optimal number of hidden layers and hidden nodes has a significant contribution to the performance of a neural network, though it is typically decided in an adhoc manner. In this paper, the structure of a neural network is adaptively optimised by determining the number...
Accurate classification of caller interaction within Interactive Voice Response systems would assist corporations to determine caller behaviour within these solutions. This paper proposes an application, which employs artificial neural networks that could assist contact centers to determine caller activity within their automated systems. Multi-layer perceptron and Radial Basis Function neural network...
The support vector machines have been promising methods for classification because of their solid mathematical foundation. However they are not favored for large-scale because the training complexity of SVM is highly dependent on the size of data set.This paper uses Incremental Reduced Support Vector Machines (IRSVM) which begins with an extremely small reduced set and incrementally expands the reduced...
The fuzzy support vector machines (FSVMs) can be used to deal with multiclass classification problems where the key issue is to solve a quadratic programming problem. This paper introduces a new fuzzy multiclass support vector machines (FMSVMs) based on compact description of data, which extends the exiting support vector machine method to the case of k-class problem in one optimization task (quadratic...
The generalization problem of an ANN classifier with unlimited size of training sample, namely asymptotic optimization in probability, is discussed in this paper. As an improved ANN network model, the pre-edited ANN classifier shows better practical performance than the standard one. However, no related theoretical research has been conducted on it. To provide a theoretical support for the pre-edited...
Flight delay early warning can reduce the negative impact of the delay. Determining the delay grade of each interval is essentially a multi-class classification problem. This paper presents a flight delay early warning model based on a fuzzy support vector machine with weighted margin (WMSVM) , which adjust the penalties to samples and the margins between samples and the hyperplane according to the...
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