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SVM is powerful for the problem with small samples, non linear and high dimension. But such important parameters as the kernel function parameters, the insensitive parameters and the penalty coefficient are determined based on experience and cross-validation in the SVM, so it has certain blindness. In the paper, support vector machine optimized particle algorithm is used to predict the intensity of...
Synthetic aperture radar (SAR) is well adapted to detect ocean pollution independently from daily or weather conditions. Oil slicks have a specific impact on ocean wave spectra because the presence of oil slicks can induce a damping of the backscattering to the sensor and a damping of the energy of wave spectra. Thus oil slicks can be discernible from the radar image. Several algorithms are applied...
A method to determine C,γ , the hyper-parameters, range for Radial Basis Function Support Vector Machines (RBF SVMs) is proposed. The γ range is determined by the extreme Squared Euclidean Distance (SED) quantiles of the training set, and the C range is determined by one pass whole training set training decreasingly along logγmax to the over-regularized limit first and increasingly along logγmedian...
As compared with text spam, the image spam is a variant which is invented to escape from traditional text-based spam classification and filtering. Various approaches to image spam filtering have been proposed with respective advantages and drawbacks in terms of time cost and efficiency. In this paper, we propose a new approach based on Base64 encoding of image files and n-gram technique for feature...
This paper presents the results achieved by fault classifier ensembles based on a model-free supervised learning approach for diagnosing faults on oil rigs motor pumps. The main goal is to compare two feature-based ensemble construction methods, and present a third variation from one of them. The use of ensembles instead of single classifier systems has been widely applied in classification problems...
This work presents the concept of Continuous Search (CS), which objective is to allow any user to eventually get their constraint solver achieving a top performance on their problems. Continuous Search comes in two modes: the functioning mode solves the user's problem instances using the current heuristics model; the exploration mode reuses these instances to train and improve the heuristics model...
In order to improve the accuracy of multi-moving objects detection in surveillant video, this paper presents a new method of detection and segmentation for moving objects based on SVM (support vector machine). To further enhance the accuracy of segmentation using support vector machine, we modify the kernel function based on its nature, and some experiments have been done to compare with other kernel...
During analog circuit synthesis in nanometer technology, process variability analysis is mandatory during design space exploration. This would ensure that the circuit will function as per specifications after fabrication even with impact of statistical variations in nanometer regimes. The methodology necessitates the evaluation of performance metrics of an analog circuit for different sizing instances...
We give sub linear-time approximation algorithms for some optimization problems arising in machine learning, such as training linear classifiers and finding minimum enclosing balls. Our algorithms can be extended to some kernelized versions of these problems, such as SVDD, hard margin SVM, and L2-SVM, for which sub linear-time algorithms were not known before. These new algorithms use a combination...
Voice is the natural communication system used by all beings, human beings in particular. Understanding and recognizing human uttered voice for various applications is the core technology of "information" age. Automatic speech recognition has wide spread applications in real life situations. Here speech recognition of Malayalam isolated digit is created by using Mel Frequency Cepstral Coefficients...
In this paper, we present the problem of appropriate feature selection for constructing a Maximum Entropy (ME) based Named Entity Recognition (NER) system under the multiobjective optimization (MOO) framework. Two conflicting objective functions are simultaneously optimized using the search capability of MOO. These objectives are (i). the dimensionality of features, which is tried to be minimized,...
The following topics are dealt with: linear approximation; license plate recognition; color image segmentation; image quantization; wireless video transmission; congestion control; stochastic search; transmembrane helical segments; wavelet transform; semisupervised cluster algorithm; anomaly detection; data privacy; online market information processing; user behavior; particle swarm optimization;...
We generalize algorithms from computational learning theory that are successful under the uniform distribution on the Boolean hypercube {0,1}n to algorithms successful on permutation invariant distributions. A permutation invariant distribution is a distribution where the probability mass remains constant upon permutations in the instances. While the tools in our generalization mimic those used for...
Breast cancer continues to be a significant public health problem in the world. Early detection is the key for improving breast cancer prognosis. Mammography has been one of the most reliable methods for early detection of breast carcinomas. However, it is difficult for radiologists to provide both accurate and uniform evaluation for the enormous mammograms generated in widespread screening. The main...
Recent work in the field of machine translation (MT) evaluation suggests that sentence level evaluation based on machine learning (ML) can outperform the standard metrics such as BLEU, ROUGE and METEOR. We conducted a comprehensive empirical study on support vector methods for ML-based MT evaluation involving multi-class support vector machines (SVM) and support vector regression (SVR) with different...
Classification is a widely used mechanism for facilitating Web service discovery. Existing methods for automatic Web service classification only consider the case where the category set is small. When the category set is big, the conventional classification methods usually require a large sample collection, which is hardly available in real world settings. This paper presents a novel method to conduct...
With the boom of web and social networking, the amount of generated text data has increased enormously. Much of this data can be considered and modeled as a stream and the volume of such data necessitates the application of automated text classification strategies. Although streaming data classification is not new, considering text data streams for classification purposes has been extensively researched...
Aimed at the research on freeway detection algorithm has great significance for improving efficiency and effectiveness of freeway traffic management, this paper based on the freeway traffic flow's characteristics, in accordance with the incident detection's basic principle, researches on freeway incident detection based on Support Vector Machine (SVM). This paper designs four different simulation...
The following topics are dealt with: data hiding techniques; wavelet transform; management information system; image sequence compressing algorithm; trusted software; case-based reasoning system; education information platform; UML; multiobjective optimization; information retrieval; web mining; CUDA architecture; fuzzy association rules; BP neural network; network security detection method; adaptive...
The Covering algorithm is proposed by Professor ZhangLing and ZhangBo in the 20th century, which simulates the structure of human learning, building a Constructive Neural Network Learning Model. Covering algorithm has been widely used to solve massive data classification problem, because its performance. The covering classification algorithm has fast learning, high recognition rate, massive data processing...
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