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As an important preprocessing technology in patent knowledge utilization, patent classification should be accurate and efficient. Commonly used feature selection methods and classification algorithms, like information gain (IG) and k nearest neighbors (k-NN) algorithm, are superior in text classification but have some drawbacks in patent classification. In the paper, we focus on patent classification...
In recent years, the MP3 music objects become the popular type of music file in many internet audio applications, including the surveillance system. But, less attention was received to the content-based classification of audio data. While Cloud Services were blooming, the classification of MP3 music has better more and more important. It is necessary to process much audio data when Cloud Computing...
When the ship is damaged after weapon attack, it is necessary for commanders to recognise its unsinkability grade quickly. Through unsinkability classification, we can know whether the ship will sink or not and its sinking probability. The unsinkability classification is a N-class pattern recognition problem. The fuzzy support vector machine (FSVM) is used to distinguish a certain unsinkability grade...
In the field of imbalance learning and cost sensitive learning, minimization of the classification error rate is not an appropriate approach due to class skew and cost distributions. Thus the area under the ROC Curve (AUC) has been widely utilized to assess the performance of the classifiers in such cases. The Maximum AUC Linear Classifier (MALC), aiming at maximizing AUC directly, is a nonparametric...
A new method for detecting and classifying loudspeaker faults is presented in this paper. Total response of high-order harmonics groups is measured and used as defect features of loudspeaker. Based on support vector machine (SVM), we built a classification system combined with one-class SVM and Directed Acyclic Graphic SVM (DAGSVM). Comparing with K-nearest neighbor (k-NN) classifier, the accuracy...
This paper proposed an efficient model lp-support vector classification(lp-SVC) which combines C-SVC and feature selection strategy by introducing the lp-norm (0 <; p <; 1). Following a lower bound for the absolute value of nonzero entries in every local optimal solution of the model, we investigated the relationship between sparsity of the solution and the choice of the regularization parameter...
SVM has been used in speaker identification successfully, whereas training SVM consumes long computing time and large memory with all training data, therefore the training data selection (TDS) is an important step for effective speaker identification system. In this paper, a novel TDS method based on the PCA and improved ant colony cluster (IACC) is proposed to solve this problem existed in SVM. The...
In many areas of pattern recognition and machine learning, subspace selection is an essential step. Fisher's linear discriminant analysis (LDA) is one of the most well-known linear subspace selection methods. However, LDA suffers from the class separation problem. The projection to a subspace tends to merge close class pairs. A recent result, named maximizing the geometric mean of Kullback-Leibler...
Along with the rapidly development of the information retrieval and web technology, web entity retrieval has become a new popular way for getting specific information, such as looking for a book or a movie. Like document retrieval, generally there are too many results returned for a query, so ranking is still a necessary step during the entity retrieval process. This paper will focus on the ranking...
With the widespread of Internet application, more and more enterprises build their Web sites and provide business information through Web pages. Web page classification could be used to assign the enterprise Web pages to one or more predefined business categories. On the purpose of Internet-based enterprises administration in E-government system, algorithms and application related to web page classification...
The imbalanced data set has been reported to hinder the classification performance of many machine learning algorithms on both accuracy and speed. But extremely imbalanced data sets (3~5% positive samples) are common for many applications, such as multimedia semantic classification. In this paper, we propose a novel algorithm to automatically remove samples that have no or negative effects on classifier...
AdaBoost is a well-known ensemble learning algorithm that generates weak classifiers sequentially and then combines them into a strong one. Also it shows its resistance to overfitting in low noise data cases , a lot of experiments have shown that it is quite sensitive on noisy data. Several modifications to AdaBoost have been proposed to deal with noisy data. Bagging and Random forests have shown...
In recent years, extensive researches have been conducted to develop approaches to answer two major challenges for collaborative filtering problems, namely sparsity and scalability. In this paper, we propose a novel collaborative filtering recommendation approach to alleviate these challenges. Our approach firstly converts the user-item ratings matrix to user-class matrix, and hence increases greatly...
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