Support vector machines (SVMs) are traditionally considered to be the best classifiers in terms of minimizing the empirical probability of misclassification, although they can be slow when the training datasets are la...
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ISBN:
(纸本)9783319255309;9783319255293
Support vector machines (SVMs) are traditionally considered to be the best classifiers in terms of minimizing the empirical probability of misclassification, although they can be slow when the training datasets are large. Here SVMs are compared to the classic k-Nearest Neighbour (k-NN) decision rule using seven large real-world datasets obtained from the University of California at Irvine (UCI) machinelearning Repository. To counterbalance the slowness of SVMs on large datasets, three simple and fast methods for reducing the size of the training data, and thus speeding up the SVMs are incorporated. One is blind random sampling. The other two are new linear-time methods for guided random sampling which we call Gaussian Condensing and Gaussian Smoothing. In spite of the speedups of SVMs obtained by incorporating Gaussian Smoothing and Condensing, the results obtained show that k-NN methods are superior to SVMs on most of the seven data sets used, and cast doubt on the general superiority of SVMs. Furthermore, random sampling works surprisingly well and is robust, suggesting that it is a worthwhile preprocessing step to either SVMs or k-NN.
Paper Since Chord progression is the element that determines the harmony of a piece of music, Automatic Chordrecognition (ACR) from audio signals is a crucial task in the field of Music Information Retrieval(MIR). Re...
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ISBN:
(纸本)9781665425803
Paper Since Chord progression is the element that determines the harmony of a piece of music, Automatic Chordrecognition (ACR) from audio signals is a crucial task in the field of Music Information Retrieval(MIR). Recently, various models using deep learning have been proposed, but there are few studies on their input features. Notes parts of the chord are the fundamental note, and its overtone ringed simultaneously. In order to model these audio signals efficiently, feature transforms such as "Constat-Q-Transform(CQT)" is used. However, due to the super-position of fundamental notes and overtones of various instruments in polyphonic music, it is considered difficult to model chords even by deep learning. Therefore, we focused on the structure, including fundamental notes are on the logarithm and its overtones are on the linear. In this paper, we propose a feature representation that can represent overtone structure for each fundamental note. Based on these feature representations, data-driven approach to learn the chord by CNN-LSTM model. We evaluated performance using 383 songs with publicly available annotations, and achieved the same performance with approximately one-tenth of the number of parameters than the existing methods.
As a new unsupervised learning algorithm to model complex data in various applications, the Generative Adversarial Networks (GANs) have gained extensive recognition and become a research hot. Inspired by the two-perso...
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ISBN:
(纸本)9781665417907
As a new unsupervised learning algorithm to model complex data in various applications, the Generative Adversarial Networks (GANs) have gained extensive recognition and become a research hot. Inspired by the two-person zero-sum game theory, the GANs methods have more powerful feature learning and feature expression ability than traditional machinelearning algorithms, while still suffering from several challenges such as the non-convergence, vanishing, and exploding gradients, modal collapse, and instability problems at the model training stage. In this paper, we firstly introduce the theory and framework of GANs and analyze the causes for the above difficulties in detail. Then we survey a wide range of typical improved GANs models by summarizing their Advantages and limitations. Finally, we discuss the existing problems in the application and possible research areas in the future.
data analysis and mining play an important role in the research of intelligent information management system, but there is a problem of inaccurate information management. Traditional machinelearning cannot solve the ...
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This paper addresses the analysis of machinelearning (ML) effectiveness in learning analytics context. Four different machinelearning approaches are evaluated. The results offer information about the usefulness of t...
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A decision support system based on datamining (DM) and Bayesian belief networks (BBN) is proposed to predict the student learning outcomes and takes the calculus course as an example to help students overcome their l...
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We face the problem of novelty detection from stream data, that is, the identification of new or unknown situations in an ordered sequence of objects which arrive on-line, at consecutive time points. We extend previou...
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ISBN:
(纸本)9783642030697
We face the problem of novelty detection from stream data, that is, the identification of new or unknown situations in an ordered sequence of objects which arrive on-line, at consecutive time points. We extend previous solutions by considering the case of objects modeled by multiple database relations. Frequent relational patterns are efficiently extracted at each time point, and a time window is used to filter out novelty patterns. An application of the proposed algorithm to the problem of detecting anomalies in network traffic is described and quantitative and qualitative results obtained by analyzing real stream of data collected from the firewall logs are reported.
The new approach of relevant feature selection in machinelearning is proposed for the case of ordered features. Feature selection and regularization of decision rule are combined in a single procedure. The selection ...
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ISBN:
(纸本)9783642030697
The new approach of relevant feature selection in machinelearning is proposed for the case of ordered features. Feature selection and regularization of decision rule are combined in a single procedure. The selection of features is realized by introducing weight coefficients, characterizing degree of relevance of respective feature. A priori information about feature ordering is taken into account in the form of quadratic penalty or in the form of absolute Value penalty on the difference of weight coefficients of neighboring features. Study of a penalty function in the form of absolute value shows computational complexity Of Such formulation. The effective method of solution is proposed. The brief survey of authors early papers. the mathematical frameworks, and experimental results are provided.
The aircraft cargo industry still maintains vast amounts of the maintenance history of aircraft components in electronic (i.e. scanned) but unsearchable images. For a given supplier, there can be hundreds of thousands...
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ISBN:
(纸本)9781450375511
The aircraft cargo industry still maintains vast amounts of the maintenance history of aircraft components in electronic (i.e. scanned) but unsearchable images. For a given supplier, there can be hundreds of thousands of image documents only some of which contain useful information. Using supervised machinelearning techniques has been shown to be effective in recognising these documents for further information extraction. A well known deficiency of supervised learning approaches is that annotating sufficient documents to create an effective model requires valuable human effort. This paper first shows how to obtain a representative sample from a supplier's corpus. Given this sample of unlabelled documents an active learning approach is used to select which documents to annotate first using a normalised certainty measure derived from a soft classifier's prediction distribution. Finally the accuracy of various selection approaches using this certainty measure are compared along each iteration of the active learning cycle. The experiments show that a greedy selection method using the uncertainty measure can significantly reduce the number of annotations required for a certain accuracy. The results provide valuable information for users and more generally illustrate an effective deployment of a machinelearning application.
Artificial intelligence has faced a rapid growth and it has made a huge change in this world. In real time the traditional algorithm has go phut to reach the human demands. Due to this great success has been gained by...
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