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A method for license plate location based on SVM is proposed in this paper. Firstly, the mathematical models of SVM are introduced, and then the feature extractions of license plate image are discussed, finally it is proved that this location method is very precise and efficient by experiments in VC development environment.
Based on the text information processing, we have made a study on the application of support vector machine in text categorization. Through introducing the basic principle of SVM, we described the process of text classification and further proposed a SVM-based classification model. Finally, experimental data show that F1 value of SVM classifier has reached more than 86.26%, and the classification...
Radar high resolution range profile (HRRP) is sensitive to the target aspect and highly overlapped in feature space between different targets, thereby hybrid features are suitable for representing the target's property. In this paper, the quadratic spline wavelet with compact support properties was used to extract the energy spectrum features of HRRP by multi-resolution decomposition, and the power...
In this paper, feature selection was carried out for multi-intelligence classification, and finds key regions. We designed different multi-intelligence tasks with BCI. SVM was used to classify and select features. The experiment reveals that a band has a greater effect on imagery intelligent tasks. And the introduced feature selection algorithm succeeded to detect key regions for multi-intelligence...
This paper addresses the problem of automatic wavelet feature extraction for signal classication. We propose to jointly learn wavelet-based features (including scale and translation of the wavelet as well as its shape) and a decision function by casting the problem as a Multi-Kernel Learning problem. A novel active constraints algorithm is then proposed. Our method has been tested on a toy dataset...
Face recognition plays an every important role in security surveillance, secure access and identity authentication. In this paper, we propose a novel face recognition method based on supervised learning. Our method consists in first extracting face feature using a supervised spectral regression, then we use multiple kernel SVM to classify face. Experimental results on Yale B face database and AR face...
In vector space models, traditional relevance feedback techniques, which utilize the terms in the relevant documents to enrich the user's initial query, is an effective method to improve retrieval performance. However, in this process, it also brings some non-relevance terms in the relevant documents in the new query. The number of non-relevance terms will increase according to the repeat of feedback...
Automatic document classification due to its various applications in data mining and information technology is one of the important topics in computer science. Classification plays a vital role in many information management and retrieval tasks. Document classification, also known as document categorization, is the process of assigning a document to one or more predefined category labels. Classification...
In this paper, features of steel defects data are selected using a wrapper algorithm to increase classification performance. The data are constructed using images of steel defects which are classified two classes as defects and pseudo defects. The suggested algorithm selects features which are relevant to class using the kappa statistic. This measure is suggested to improve accuracy of minor class...
The empirical results of investigating vocal correlate of depression in female adults are presented in that the certain acoustical property of spoken sound based on spectral entropy is capable of relating the affect change in speech with the symptom severity in diagnostic speakers. Studied sub-band entropies achieved the 93% correct classification in classifying two classes of depressed and remitted...
This work focuses on the recognition of three-dimensional colon polyps captured by an active stereo vision sensor. The detection algorithm consists of SVM classifier trained on robust feature descriptors. The study is related to Cyclope, this prototype sensor allows real time 3D object reconstruction and continues to be optimized technically to improve its classification task by differentiation between...
Polyspectral feature extraction is considered to be a potential method for individual communication transmitter identification. However, the curse of dimensionality caused by higher orders of the features restrains the efficiency of classification. A new method using support vector machine with kernels of polyspectra is present for classification of individual transmitters. The result of experiments...
In this paper, a novel approach combining kernel principal component analysis (KPCA) and least square support vector machine (LSSVM) is proposed for HVAC fan machinery status monitoring and fault diagnosis, which combines KPCA for fault feature extraction and multiple SVMs (MSVMs) for identification of different fault sources. KPCA is used as a preprocessor of LSSVM, which maps the original input...
In order to resolve the problem incurred by low efficient manual classification of tremendous aurora images, an automatic aurora images classification system for huge dataset application is proposed. First, static aurora images are decomposed into texture part and cartoon part with a method called Morphological Component Analysis (MCA). Then features extracted from texture part are classified by three...
In this paper, we propose a speech emotion recognition system using both spectral and prosodic features. Most traditional systems have focused on spectral features or prosodic features. Since both the spectral and the prosodic features contain emotion information, it is believed that the combining of spectral features and prosodic features will improve the performance of the emotion recognition system...
Feature extraction is an important task before weapon system cost forecasting modeling, which affects the forecasting performance of the model. In this paper, feature extraction in the weapon system cost forecasting was studied. In regard to the mechanism of feature extraction and the good performance of support vector machine (SVM), principal components analysis (PCA) and kernel principal components...
This study proposes a new strategy combining with the SVM(support vector machine) classifier for features selection that retains sufficient information for classification purpose. Our proposed approach uses F-score models to optimize feature space by removing both irrelevant and redundant features. To improve classification accuracy, the parameters optimization of the penalty constant C and the bandwidth...
Relationship extraction (RE) from biomedical literature is an important and challenging problem in both text mining and bioinformatics. Although various approaches have been proposed to extract protein-protein interaction types, their accuracy rates leave a large room for further exploration of more effective methods. In this paper, two supervised learning algorithms based on newly-defined ldquobio-semantic...
The study of the behavior of ion-channels can provide significant information to detect metal ions and small organic molecules in solution. Discrimination of different analytes can be performed by extracting appropriate features from the ion-channel signals and using them for classification. In this paper, we consider features extracted from the Fourier, Wavelet and Walsh-Hadamard domain representations...
In most parts of the world, the quality of the electrical power has become a major concern for many electricity users especially the industrial customers. To the power utility, all power quality disturbances must be detected, classified and diagnosed accurately so that proper mitigation measures can be implemented. This paper presents the application of the S-transform and support vector machine (SVM)...
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