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Web text classification is the process of determine the text types automatically under a given classification, according to the text content. Web text categorization system is the use of machine learning, knowledge engineering and other related fields of knowledge, access to the web on the text, after text preprocessing, Chinese word segmentation and training classifier, using classification algorithm...
To solve the difficulty of the field of Automatic Entity Relation Extraction, in this paper, a method that used binary classification thinking, meanwhile combined with reasoning rules to extract the field of entity relation is proposed. considering comprehensively the context information of entity, entity type and their combination of characteristics to construct the feature set, which in order to...
Aimming at the ever-present problem of imbalanced data in text classification, the authors study on several forms of imbalanced data, such as text number, class size, subclass and class fold. Some useful conclusions are gotten from a series of correlative experiments: first, when the text of two class is almost the same number, the difference of word number become major factor to affect the accuracy...
In this work, we proposed a novel authentication system based on facial features. The proposed method is based on PCA and LDA for feature extraction, these extracted features are combined using wavelet fusion. In this work we use neural networks to classify extracted features of faces. The proposed method consists of six steps: i) Extraction of images from the database, ii) Preprocessing, iii) Feature...
This paper describes and deduces the theory of Haar-like features, Integral image and AdaBoost algorithm, which were proposed by Paul Viola, and then researches its improvement. We combine Microsoft Visual C++6.0 with OpenCV Function library to develop the software, and achieve the function of real-time face detection. According to experimental results, we can conclude that the improved algorithm...
This paper describes a novel technique for detecting a human body direction using SVM constructed by HOG feature selected by AdaBoost. HOG feature is well-known feature for the robust judgment of a human. We employ the feature for detecting a human body direction. We compared some feature selecting methods with the previous one. Experimental results show effectiveness of the proposed method.
Abstract-By analyzing the process of classification and MapReduce computing paradigms, it is found that the parallel and distributed computing model in MapReduce is appropriate for constructing classifier model. This paper presents a MapReduce algorithm for parallel and distributed classification, aiming to reduce the computational time in training process on large scale documents. Our experiment...
Recent advances in neuroimaging demonstrate the potential use of functional near infrared spectroscopy (fNIRS) in the field of brain machine interface. An fNIRS uses light in the near infrared range to measure brain surface hemoglobin concentrations to determine a neural activity. The current study presents our empirical results in realizing fNIRS - BCI system. We analyze the hemodynamic responses...
In the last decade significant progress in computer vision based control of unmanned ground vehicles (UGV) has been achieved. However, until now textural information has been somewhat less effective than color or laser range information. In this paper we propose a computer vision based cross country segmentation system that is capable of distinguishing cross-country road, grass and trees during day-time...
Vocabulary Tree (VT) is one of offline learning-classifier to deal with large number of image set efficiently by combining bag of words concept with tree structure. Bag of words concept makes our classifier possible to return robust classification results regardless of image size, rotation, and other noises. Tree structure can give us very fast classification testing time. But, because of limitation...
This paper presents a new neural network architecture kernel principal component neural network (KPCNN) trained by threshold accepting based training algorithm with different kernels like polynomial, sigmoid and Gaussian and its application to bankruptcy prediction in banks. KPCNN is a non linear version of the PCNN proposed elsewhere. In this architecture, dimensionality reduction is taken care of...
Artificial neural networks are significantly used in the field of ophthalmology for accurate disease identification which further aids in treatment planning. In this paper, an automated system based on Self-Organizing neural network (Kohonen network) is proposed for eye disease classification. Abnormal retinal images from four different classes namely non-proliferative diabetic retinopathy (NPDR),...
The increasing complexity of modern distributed systems makes conventional fault tolerance and recovery prohibitively expensive. One of the promising approaches is online failure prediction. However, the process of feature extraction depends on the experienced administrators and their domain knowledge to filtering and compressing error events into a form that is easy for failure prediction. In this...
The paper proposes a method for the classification of EEG signal based on machine learning methods. We analyzed the data from an EEG experiment consisting of affective picture stimuli presentation, and tested automatic recognition of the individual emotional states from the EEG signal using Bayes classifier. The mean accuracy was about 75 percent, but we were not able to select universal features...
Locating and identifying complex objects in a visual scene is a typical problem within the areas of computer vision and image analysis. One technique to minimise the size of image to be identified is to base the classification on smaller features of the image, which are combined into a more complex structure to identify the complete object. For example, locating two eyes, a nose and a mouth can enable...
The feature subset selection is a key preprocessing part in the detection of the stored-grain insects based on the image recognition technology. According to the global optimization ability of the particle swarm optimization (PSO) and the superior classification performance of the support vector machines (SVM), this study proposed a method based on PSO and SVM to improve the classification accuracy...
This paper introduces a novel algorithm for features selection based on a Support Vector Decision Function (SVDF) and Forward Selection (FS) approach with a fuzzy inferencing model. In the new algorithm, Fuzzy Enhancing Support Vector Decision Function (Fuzzy ESVDF), features are selected stepwise, one at a time, by using SVDF to evaluate the weight value of each specified candidate feature, then...
Under the framework of PU(Positive data and Unlabeled data), this paper originally proposes a three-setp algorithm. First, CoTraining is employed for filtering out the likely positive data from the unlabeled dataset U. Second, affinity propagation (AP) approach attempts to pick out the strong positive from likely positive set which is produced in first step. Those data picked out can be supplied to...
A detailed design and implementation of a Chinese Web-page classification system is described in this paper, and some methods on Chinese Web-page preprocessing and feature preparation are proposed. Experimental results on a Chinese Web-page dataset show that methods we designed can improve the performance from 75.82% to 81.88%.
Data classification has been studied widely in the fields of Artificial Intelligence, Machine Learning, Data Mining and Pattern Recognition. Up to the present, the development of classification has made great achievements, and many kinds of classified technology and theory will continue to emerge. This paper discusses a great deal of classification algorithms based on the Artificial Neural Networks,...
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