Music recommendation is an popular function for personalized services and smart applications since it focuses on discovering users’ leisure preference. The traditional music recommendation strategy captured users’ m...
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Single-relation factoid question answering (QA) is strongly supported by rich sources of facts from knowledge bases (KB). However, there are many irrelevant information in questions and overwhelming number of facts in...
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Sparse subspace learning has been demonstrated to be effective in data mining and machine learning. In this paper, we cast the unsupervised feature selection scenario as a matrix factorization problem from the viewpoi...
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Sparse subspace learning has been demonstrated to be effective in data mining and machine learning. In this paper, we cast the unsupervised feature selection scenario as a matrix factorization problem from the viewpoint of sparse subspace learning. By minimizing the reconstruction residual, the learned feature weight matrix with the l 2,1 -norm and the non-negative constraints not only removes the irrelevant features, but also captures the underlying low dimensional structure of the data points. Meanwhile in order to enhance the model's robustness, l 1 -norm error function is used to resistant to outliers and sparse noise. An efficient iterative algorithm is introduced to optimize this non-convex and non-smooth objective function and the proof of its convergence is given. Although, there is a subtraction item in our multiplicative update rule, we validate its non-negativity. The superiority of our model is demonstrated by comparative experiments on various original datasets with and without malicious pollution.
Considering the distinctiveness of different group features in the sparse representation, a novel joint multi- task and weighted group sparsity （JMT-WGS） method is pro- posed. By weighting popular group sparsity, no...
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Considering the distinctiveness of different group features in the sparse representation, a novel joint multi- task and weighted group sparsity （JMT-WGS） method is pro- posed. By weighting popular group sparsity, not only the rep- resentation coefficients from the same class over their asso- ciate dictionaries may share some similarity, but also the rep- resentation coefficients from different classes have enough di- versity. The proposed method is cast into a multi-task frame- work with two-stage iteration. In the first stage, representa- tion coefficient can be optimized by accelerated proximal gra- dient method when the weights are fixed. In the second stage, the weights are computed via the prior information about their entropy. The experimental results on three facial expres- sion databases show that the proposed algorithm outperforms other state-of-the-art algorithms and demonstrate the promis- ing performance of the proposed algorithm.
The application of online examination is used more and more widely. Test paper composition is the core function in this application. Online examination system requires that the test paper should be quick and flexible,...
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The application of online examination is used more and more widely. Test paper composition is the core function in this application. Online examination system requires that the test paper should be quick and flexible, and the composition of questions should be reasonable and random. Test paper should not only meet the specific needs of users, ensure fairness, impartiality, but also be accurate and efficient. The intelligent test paper composition algorithm based on genetic algorithm is studied in this paper. Then this method is used to organize the structure of the test paper automatically, composites the examination content. Proposals are put forward to examines the degree of students' mastery of knowledge based on this method. This algorithm can automatically composite test papers according to the conditions of difficulty degree, knowledge coverage, and the proportion of questions. It can automatically composite test papers for online assessment. The practice of online testing system based on this algorithm shows that the algorithm is scientific and effective. On the premise of guaranteeing the quality of the test paper, it greatly improves the efficiency and pertinence of online testing. It is beneficial to improve students' learning efficiency and intellectualization of education.
Feature selection plays an important role in data mining and recognition, especially in the large scale text, image and biological data. Specifically, the class label information is unavailable to guide the selection ...
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News system requires news classification and personalized recommendation to improve user's efficiency and interest, and to enhance user's experiences. This paper constructed a news automatic classification and...
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News system requires news classification and personalized recommendation to improve user's efficiency and interest, and to enhance user's experiences. This paper constructed a news automatic classification and recommendation system through natural language processing, text classification, collaborative filtering algorithm. The published news contents were word-segmented and model-trained automatically first to determine which category the news belonging to. Users can also manually modify the classification so that later classification can be updated and improved. After that, the similarity between users was calculated by collaborative filtering and the users having higher similarity with the recommended users were selected. The news seen by the certain users were recommended to the users that were divided into the same group. This paper takes the news corpus of Fudan University's text classification research center as experimental data. Text classification accuracy is tested by this corpus. The experimental results show that the system can serve the news users well. It achieves effective classification and recommendation of news personally.
Text passwords are the most widely used authentication methods and will also be used in the future. Text passwords can be regarded as meaningful strings, and deep learning methods have an advantage of text processing....
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ISBN:
(数字)9781665403924
ISBN:
(纸本)9781665403931
Text passwords are the most widely used authentication methods and will also be used in the future. Text passwords can be regarded as meaningful strings, and deep learning methods have an advantage of text processing. LSTM, RNN, GAN and other deep learning models have been using in password guessing and password strength measurements. In the paper, we make a survey on state-of-the-art deep learning methods for password guessing and password strength evaluation, including password pattern extraction, candidate password generation and password strength measurement. Compared with traditional methods, neural networks based methods can achieve better results and performance.
Clinical decision support is a probabilistic and quantitative method learning used for problem modeling in situations of ***,mathematical methods such as probability learning and statistical analysis is increasingly u...
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ISBN:
(纸本)9781509036202
Clinical decision support is a probabilistic and quantitative method learning used for problem modeling in situations of ***,mathematical methods such as probability learning and statistical analysis is increasingly used in the medical field to assure the accuracy of the diagnoses and optimize the *** study describes the design and implementation of an improved weighted Na?ve Bayes classifier based on the idea that there is a certain level of dependency between *** the coronary artery disease,clinical data and patients' medical records were used to train the improved Na?ve Bayes classifier to compute the possible disease manifestations,the priori probability and the posterior probability and to carry out the disease ***,we were able to improve the precision of the improved weighted Na?ve Bayes classifier to 79%(increase of 12% compared to Na?ve Bayes).The suitability of the improved Na?ve Bayes classifiers for carrying out diagnosis allows them to be utilized to distinguish syndromes and signs based on unobserved evidence.
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