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34 results on '"Zhou, Zhi-hua"'

3. Margin Distribution Analysis.

Author

Wang, Jun and Zhou, Zhi-Hua

Subjects
*MACHINE learning, *LINEAR equations, *CONCEPT learning
Abstract

Margin is an important concept in machine learning; theoretical analyses further reveal that the distribution of margin plays a more critical role than the minimum margin in generalization power. Recently, several approaches have achieved performance breakthroughs by optimizing the margin distribution, but their computational cost, which is usually higher than before, still hinders them to be widely applied. In this article, we propose margin distribution analysis (MDA), which optimizes the margin distribution more simply by maximizing the margin mean and minimizing the margin variance simultaneously. MDA is efficient and resistive to class-imbalance naturally, since its objective distinguishes the margin means of different classes and can be broken up into two linear equations. In practice, it can also cooperate with other frameworks such as reweight-minimization when facing complex circumstances with noise and outliers. Empirical studies validate the superiority of MDA in real-world data sets, and demonstrate that simple approaches can also perform competitively by optimizing margin distribution. [ABSTRACT FROM AUTHOR]

Published
2022
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4. Optimal Margin Distribution Machine.

Author

Zhang, Teng and Zhou, Zhi-Hua

Subjects
*SUPPORT vector machines, *MACHINE learning, *ORDER statistics
Abstract

Support Vector Machine (SVM) has always been one of the most successful learning algorithms, with the central idea of maximizing the minimum margin, i.e., the smallest distance from the instances to the classification boundary. However, recent theoretical results disclosed that maximizing the minimum margin does not necessarily lead to better generalization performance, and instead, the margin distribution has been proven to be more crucial. Based on this idea, we propose the Optimal margin Distribution Machine (ODM), which can achieve a better generalization performance by optimizing the margin distribution explicitly. We characterize the margin distribution by the first- and second-order statistics, i.e., the margin mean and variance. The proposed method is a general learning approach which can be applied in any place where SVMs are used, and its superiority is verified both theoretically and empirically in this paper. [ABSTRACT FROM AUTHOR]

Published
2020
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5. One-Pass Learning with Incremental and Decremental Features.

Author

Hou, Chenping and Zhou, Zhi-Hua

Subjects
*DETECTORS, *ENVIRONMENTAL monitoring, *INSTRUCTIONAL systems, *APPLIED ecology, *MOBILE games, *ENVIRONMENTAL engineering
Abstract

In many real tasks the features are evolving, with some features vanished and some other features being augmented. For example, in environment monitoring some sensors expired whereas some new ones were deployed; in mobile game recommendation some games dropped whereas some new ones were added. Learning with such incremental and decremental features is crucial but rarely studied, particularly when the data comes like a stream and thus it is infeasible to keep the whole data for optimization. In this paper, we study this challenging problem and present the OPID approach. Our approach attempts to compress important information of vanished features into functions of survived features, and then expand to include the augmented features. It is an one-pass learning approach, which only needs to scan each instance once and does not need to store the whole data, and thus satisfies the evolving streaming data nature. After tackling this problem in one-shot scenario, we then extend it to multi-shot case. Empirical study on a broad range of data sets shows that our approach can address this problem effectively. [ABSTRACT FROM AUTHOR]

Published
2018
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6. On the doubt about margin explanation of boosting.

Author

Gao, Wei and Zhou, Zhi-Hua

Subjects
*BOOSTING algorithms, *PERFORMANCE evaluation, *DISTRIBUTION (Probability theory), *GENERALIZATION, *ERROR analysis in mathematics, *CLASSIFICATION, *DIMENSIONS
Abstract

Abstract: Margin theory provides one of the most popular explanations to the success of AdaBoost, where the central point lies in the recognition that margin is the key for characterizing the performance of AdaBoost. This theory has been very influential, e.g., it has been used to argue that AdaBoost usually does not overfit since it tends to enlarge the margin even after the training error reaches zero. Previously the minimum margin bound was established for AdaBoost, however, Breiman (1999) [9] pointed out that maximizing the minimum margin does not necessarily lead to a better generalization. Later, Reyzin and Schapire (2006) [37] emphasized that the margin distribution rather than minimum margin is crucial to the performance of AdaBoost. In this paper, we first present the kth margin bound and further study on its relationship to previous work such as the minimum margin bound and Emargin bound. Then, we improve the previous empirical Bernstein bounds (Audibert et al. 2009; Maurer and Pontil, 2009) [2,30], and based on such findings, we defend the margin-based explanation against Breimanʼs doubts by proving a new generalization error bound that considers exactly the same factors as Schapire et al. (1998) [39] but is sharper than Breimanʼs (1999) [9] minimum margin bound. By incorporating factors such as average margin and variance, we present a generalization error bound that is heavily related to the whole margin distribution. We also provide margin distribution bounds for generalization error of voting classifiers in finite VC-dimension space. [Copyright &y& Elsevier]

Published
2013
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7. Improved Bounds for the Nyström Method With Application to Kernel Classification.

Author

Jin, Rong, Yang, Tianbao, Mahdavi, Mehrdad, Li, Yu-Feng, and Zhou, Zhi-Hua

Subjects
APPROXIMATION error, KERNEL functions, RANDOM matrices, EIGENVALUES, SPECTRUM analysis, CLASSIFICATION, GENERALIZATION, SUPPORT vector machines
Abstract

We develop two approaches for analyzing the approximation error bound for the Nyström method that approximates a positive semidefinite (PSD) matrix by sampling a small set of columns, one based on a concentration inequality for integral operators, and one based on random matrix theory. We show that the approximation error, measured in the spectral norm, can be improved from O(N/\sqrt m) to O(N/m^1 - \rho ) in the case of large eigengap, where N is the total number of data points, m is the number of sampled data points, and \rho \in (0, 1/2) is a positive constant that characterizes the eigengap. When the eigenvalues of the kernel matrix follow a p-power law, our analysis based on random matrix theory further improves the bound to O(N/m^p - 1) under an incoherence assumption. We present a kernel classification approach based on the Nyström method and derive its generalization performance using the improved bound. We show that when the eigenvalues of the kernel matrix follow a p-power law, we can reduce the number of support vectors to N^2p/(p^2 - 1), which is sublinear in N when p > 1+\sqrt 2, without seriously sacrificing its generalization performance. [ABSTRACT FROM AUTHOR]

Published
2013
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8. Towards convergence rate analysis of random forests for classification.

Author

Gao, Wei, Xu, Fan, and Zhou, Zhi-Hua

Subjects
*RANDOM forest algorithms, *REGRESSION trees, *RANDOM numbers, *CLASSIFICATION
Abstract

Random forests have been one of the successful ensemble algorithms in machine learning, and the basic idea is to construct a large number of random trees individually and make predictions based on an average of their predictions. The great successes have attracted much attention on theoretical understandings of random forests, mostly focusing on regression problems. This work takes one step towards the convergence rates of random forests for classification. We present the first finite-sample rate O (n − 1 / (8 d + 2)) on the convergence of purely random forests for binary classification, which can be improved to be of O (n − 1 / (3.87 d + 2)) by considering the midpoint splitting mechanism. We introduce another variant of random forests, which follows Breiman's original random forests but with different mechanisms on splitting dimensions and positions. We present the convergence rate O (n − 1 / (d + 2) (ln ⁡ n) 1 / (d + 2)) for the variant of random forests, which reaches the minimax rate, except for a factor (ln ⁡ n) 1 / (d + 2) , of the optimal plug-in classifier under the L -Lipschitz assumption. We achieve the tighter convergence rate O (ln ⁡ n / n) under some assumptions over structural data. This work also takes one step towards the convergence rate of random forests for multi-class learning, and presents the same convergence rates of random forests for multi-class learning as that of binary classification, yet with different constants. We finally provide empirical studies to support the theoretical analysis. [ABSTRACT FROM AUTHOR]

Published
2022
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9. Attention-Based Hierarchical Recurrent Neural Network for Phenotype Classification

Author

Xu, Nan, Shen, Yanyan, Zhu, Yanmin, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Pandu Rangan, C., Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Yang, Qiang, editor, Zhou, Zhi-Hua, editor, Gong, Zhiguo, editor, Zhang, Min-Ling, editor, and Huang, Sheng-Jun, editor

Published
2019
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10. Evaluating Diagnostic Performance of Machine Learning Algorithms on Breast Cancer

Author

Gatuha, George, Jiang, Tao, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, He, Xiaofei, editor, Gao, Xinbo, editor, Zhang, Yanning, editor, Zhou, Zhi-Hua, editor, Liu, Zhi-Yong, editor, Fu, Baochuan, editor, Hu, Fuyuan, editor, and Zhang, Zhancheng, editor

Published
2015
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11. Online Prediction of Chess Match Result

Author

Masud, Mohammad M., Al-Shehhi, Ameera, Al-Shamsi, Eiman, Al-Hassani, Shamma, Al-Hamoudi, Asmaa, Khan, Latifur, Goebel, Randy, Series editor, Tanaka, Yuzuru, Series editor, Wahlster, Wolfgang, Series editor, Cao, Tru, editor, Lim, Ee-Peng, editor, Zhou, Zhi-Hua, editor, Ho, Tu-Bao, editor, Cheung, David, editor, and Motoda, Hiroshi, editor

Published
2015
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13. Repulsive-SVDD Classification

Author

Nguyen, Phuoc, Tran, Dat, Goebel, Randy, Series editor, Tanaka, Yuzuru, Series editor, Wahlster, Wolfgang, Series editor, Cao, Tru, editor, Lim, Ee-Peng, editor, Zhou, Zhi-Hua, editor, Ho, Tu-Bao, editor, Cheung, David, editor, and Motoda, Hiroshi, editor

Published
2015
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15. Efficient Methods for Multi-label Classification

Author

Sun, Chonglin, Zhou, Chunting, Jin, Bo, Lau, Francis C. M., Goebel, Randy, Series editor, Tanaka, Yuzuru, Series editor, Wahlster, Wolfgang, Series editor, Cao, Tru, editor, Lim, Ee-Peng, editor, Zhou, Zhi-Hua, editor, Ho, Tu-Bao, editor, Cheung, David, editor, and Motoda, Hiroshi, editor

Published
2015
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18. A DC Programming Approach for Sparse Optimal Scoring

Author

Thi, Hoai An Le, Phan, Duy Nhat, Goebel, Randy, Series editor, Tanaka, Yuzuru, Series editor, Wahlster, Wolfgang, Series editor, Cao, Tru, editor, Lim, Ee-Peng, editor, Zhou, Zhi-Hua, editor, Ho, Tu-Bao, editor, Cheung, David, editor, and Motoda, Hiroshi, editor

Published
2015
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19. A Biclustering-Based Classification Framework for Microarray Analysis

Author

Malhotra, Baljeet, Dahlmeier, Daniel, Nandan, Naveen, Goebel, Randy, Series editor, Tanaka, Yuzuru, Series editor, Wahlster, Wolfgang, Series editor, Peng, Wen-Chih, editor, Wang, Haixun, editor, Bailey, James, editor, Tseng, Vincent S., editor, Ho, Tu Bao, editor, Zhou, Zhi-Hua, editor, and Chen, Arbee L.P., editor

Published
2014
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21. Using Knowledge Graph to Handle Label Imperfection

Author

Liu, Yi, Li, Huakang, Chen, Yizheng, Goebel, Randy, Series editor, Tanaka, Yuzuru, Series editor, Wahlster, Wolfgang, Series editor, Peng, Wen-Chih, editor, Wang, Haixun, editor, Bailey, James, editor, Tseng, Vincent S., editor, Ho, Tu Bao, editor, Zhou, Zhi-Hua, editor, and Chen, Arbee L.P., editor

Published
2014
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22. Associative Classification for Human Activity Inference on Smart Phones

Author

Peng, Yu-Hsiang, Njoo, Gunarto Sindoro, Li, Shou-Chun, Peng, Wen-Chih, Goebel, Randy, Series editor, Tanaka, Yuzuru, Series editor, Wahlster, Wolfgang, Series editor, Peng, Wen-Chih, editor, Wang, Haixun, editor, Bailey, James, editor, Tseng, Vincent S., editor, Ho, Tu Bao, editor, Zhou, Zhi-Hua, editor, and Chen, Arbee L.P., editor

Published
2014
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23. Multi-Instance Learning from Positive and Unlabeled Bags

Author

Wu, Jia, Zhu, Xingquan, Zhang, Chengqi, Cai, Zhihua, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Kobsa, Alfred, editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Weikum, Gerhard, editor, Goebel, Randy, editor, Tanaka, Yuzuru, editor, Wahlster, Wolfgang, editor, Siekmann, Jörg, editor, Tseng, Vincent S., editor, Ho, Tu Bao, editor, Zhou, Zhi-Hua, editor, Chen, Arbee L. P., editor, and Kao, Hung-Yu, editor

Published
2014
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24. Learning from Crowds under Experts’ Supervision

Author

Hu, Qingyang, He, Qinming, Huang, Hao, Chiew, Kevin, Liu, Zhenguang, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Kobsa, Alfred, editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Weikum, Gerhard, editor, Goebel, Randy, editor, Tanaka, Yuzuru, editor, Wahlster, Wolfgang, editor, Siekmann, Jörg, editor, Tseng, Vincent S., editor, Ho, Tu Bao, editor, Zhou, Zhi-Hua, editor, Chen, Arbee L. P., editor, and Kao, Hung-Yu, editor

Published
2014
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25. Highly Scalable Attribute Selection for Averaged One-Dependence Estimators

Author

Chen, Shenglei, Martinez, Ana M., Webb, Geoffrey I., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Kobsa, Alfred, editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Weikum, Gerhard, editor, Goebel, Randy, editor, Tanaka, Yuzuru, editor, Wahlster, Wolfgang, editor, Siekmann, Jörg, editor, Tseng, Vincent S., editor, Ho, Tu Bao, editor, Zhou, Zhi-Hua, editor, Chen, Arbee L. P., editor, and Kao, Hung-Yu, editor

Published
2014
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26. Automatic Fake Followers Detection in Chinese Micro-blogging System

Author

Shen, Yi, Yu, Jianjun, Dong, Kejun, Nan, Kai, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Kobsa, Alfred, editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Weikum, Gerhard, editor, Goebel, Randy, editor, Tanaka, Yuzuru, editor, Wahlster, Wolfgang, editor, Siekmann, Jörg, editor, Tseng, Vincent S., editor, Ho, Tu Bao, editor, Zhou, Zhi-Hua, editor, Chen, Arbee L. P., editor, and Kao, Hung-Yu, editor

Published
2014
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27. Feature Selection via Sparse Regression for Classification of Functional Brain Networks

Author

Wang, Yilun, Wu, Guorong, Long, Zhiliang, Sheng, Jingwei, Zhang, Jiang, Chen, Huafu, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Sun, Changyin, editor, Fang, Fang, editor, Zhou, Zhi-Hua, editor, Yang, Wankou, editor, and Liu, Zhi-Yong, editor

Published
2013
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28. Sparse Brain anatomical Network Based Classification of Schizophrenia Patients and Healthy Controls

Author

Zheng, Junjie, Wang, Yilun, Chen, Heng, Chen, Huafu, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Sun, Changyin, editor, Fang, Fang, editor, Zhou, Zhi-Hua, editor, Yang, Wankou, editor, and Liu, Zhi-Yong, editor

Published
2013
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29. Binary Decision Trees for Melanoma Diagnosis

Author

Zhou, Yu, Song, Zhuoyi, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Zhou, Zhi-Hua, editor, and Roli, Fabio, editor

Published
2013
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30. Building a Decision Cluster Forest Model to Classify High Dimensional Data with Multi-classes

Author

Li, Yan, Hung, Edward, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Zhou, Zhi-Hua, editor, and Washio, Takashi, editor

Published
2009
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34. On the noise estimation statistics.

Author

Gao, Wei, Zhang, Teng, Yang, Bin-Bin, and Zhou, Zhi-Hua

Subjects
*ESTIMATION theory, *NOISE, *COVARIANCE matrices, *DATA distribution, *K-means clustering
Abstract

Learning with noisy labels has attracted much attention during the past few decades. A fundamental problem is how to estimate noise proportions from corrupted data. Previous studies on this issue resort to the estimations of class distributions, conditional distributions, or the kernel embedding of distributions. In this paper, we present another simple and effective approach for noise estimation. The basic idea is to utilize the first- and second-order statistics of observed data, and the positive semi-definiteness of covariance matrices. Then, an upper bound on noise estimation is provided without additional assumptions over data distribution. Based on this idea and using the locality property of random noise, we develop the Noise Estimation Statistics with Clusters (NESC) method, which firstly clusters the corrupted data by k -means algorithm, and then makes noise estimation from clusters based on the first- and second-order statistics. We present the existence, uniqueness and convergence analysis of our noise estimation, and empirical studies verify the effectiveness of the NESC method. [ABSTRACT FROM AUTHOR]

Published
2021
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