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To date, there are no reliable markers for making an early diagnosis of schizophrenia before clinical diagnostic criteria are fully met. Neuroimaging and pattern classification techniques are promising tools towards predicting transition to schizophrenia. Here, we investigated the diagnostic performance of a combination of neuroanatomical and clinical data in predicting transition to schizophrenia...
Currently, network intrusion detection is in face of the conflict between the difficult to label data and the high accuracy request to detect intrusion. In this paper, we propose a SVM co-training based method to detect network intrusion. It exploits the large amount of unlabeled data, and increase the detection accuracy and stability by co-training two classifiers. The simulation results show that...
In real world classification tasks, the original instances are represented by raw features. Usually domain related algorithms are needed to extract discriminative features. But the algorithms selection and additional parameters tuning are difficult for people with little domain knowledge and experience. In this paper, a new machine learning framework called "decompose learning" is proposed...
In this paper, we present a consumption pattern recognition system based on SVM. It can produce an optimized classification pattern using SVM algorithm and use the pattern to predict consumer behaviors. In this system, three dimension reduction methods including Principal Component Analysis (PCA), correlation analysis and data cubes are applied to reduce dimension of features and two training methods...
Nowadays, text classification has been one of the key subjects in intelligent information processing. Owing to the complex features of natural language, the feature space dimensions will be particularly high. How to improve the accuracy of text classification is an important and hard problem. As rough set is a useful tool to deal with uncertain information, a hybrid algorithm for text classification...
Link spam techniques can enable some pages to achieve higher-than-deserved rankings in the results of a search engine. They negatively affect the quality of search results. Classification methods can detect link spam. For classification problem, features play an important role. This paper proposes to derive new features using genetic programming from existing link-based features and use the new features...
For classification problems, the generalized eigenvalue proximal support vector machine (GEPSVM) and twin support vector machine (TWSVM) are regarded as milestones in the development of the powerful SVMs, as they use the nonparallel hyperplane classifiers. In this brief, we propose an improved version, named twin bounded support vector machines (TBSVM), based on TWSVM. The significant advantage of...
In this paper a hierarchical structure is proposed for automatic gender identification (AGI). In this structure two clustering techniques are used. The first technique is divisive clustering for dividing speakers from each gender to some classes of speakers. The second clustering technique is agglomerative clustering for creating a hierarchical structure. Feature reduction is done by SOAP feature...
this paper presents a classification based on support vector machine (SVM) to carry out comprehensive analysis of the ability of enterprises paying debt,reduce the risk of bank to provide a loan. First this paper introduces the main principle of support vector machines to establish data classification model, using historical data for classification. Then collect the financial indices of 80 enterprises...
The increase of malware that are exploiting the Internet daily has become a serious threat. The manual heuristic inspection of malware analysis is no longer considered effective and efficient compared against the high spreading rate of malware. Hence, automated behavior-based malware detection using machine learning techniques is considered a profound solution. The behavior of each malware on an emulated...
In this paper we propose a novel Support Vector Machine(SVM) based approach for noisy data removal from datasets. It is observed that the instability present in the dataset greatly affects the overall performance of the any classifier. Hence, we propose a methodology for removal of such instabilities. In the proposed approach, we proceed by determining the clusters formed using support equilibrium...
This paper proposes a feature relation network (FRN) to model the underlying feature relation structures of a set of observations. A pattern classification system is then constructed based on the feature relation network, namely PCS-FRN. During training process, PCS-FRN will form an attractor for each group of samples in order to lower the overall energy states. The attractor, or a feature relation...
It's still in search that how to apply SVM in muti-classify. Directed Acyclic Graph is easier to be computed and has better learning effect than other arithmetic. Experimental platform is used to simulate typical faults of circumrotate machines. Based on the frequency domain feature, energy eigenvector of frequency domain is presented using wavelet packet analysis method. DAGSVM is applied in classification,...
We have recently found that the computation time of homology-based subcellular localization can be substantially reduced by aligning profiles up to the cleavage site positions of signal peptides, mitochondrial targeting peptides, and chloro-plast transit peptides [1]. While the method can reduce the profile alignment time by as much as 20 folds, it cannot reduce the computation time spent on creating...
This paper proposes a new feature-selection strategy by integrating the Rough Set Theory (RST) and Particle Swarm Optimisation (PSO) algorithms to generate a set of discriminatory features for the classification problem. The proposed method is seen as a marriage between filter and wrapper approaches in which the RST is used to pre-reduce the feature set before optimisation by PSO, a meta-heuristic...
This paper investigates lexical stress detection for Chinese learners of English, where a combined differential acoustic feature is developed to represent the lexical stress of polysyllabic words in continuous speech. The use of frame-averaged feature and the contextual information intra-word can be input to the classifiers without normalization. The word-based stress detection method proposed in...
From a new view of financial distress concept drift, this paper attempts to put forward a new method for dynamic financial distress prediction modeling based on slip time window and multiple support vector machines (SVMs). A new algorithm is designed to dynamically select the proper time window to handle concept drift, and then a dynamic classifier selection method is used to build a combined model...
Support Vector Machines, a new generation learning system based on recent advances in statistical learning theory deliver state-of-the-art performance in real-world applications such as text categorization, hand-written character recognition, image classification, bio-sequence analysis etc for the classification and regression. This paper emphasizes the classification task with Support Vector Machine...
In order to construct a high-performance ensemble classifier, it needs that the basic classifiers, which contained by the ensemble one, have higher classification precision and their classification error is independent from each other. In fact, it is too difficult to choose these basic classifiers satisfying the two conditions above. Rough reduction is the core in the fields of Rough Set theory. Each...
In this paper an improvement of wavelet based methods for detection and classification of power quality disturbances is presented. In the feature extraction process wavelet analysis is also used as in the comparing methods. However, the feature vector is extended with three other coefficients in order to improve the accuracy of the algorithm. In order to evaluate the proposed method, large number...
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