[PDF] Top 20 Fuzzy Regression Analysis by Support Vector Learning Approach
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Fuzzy Regression Analysis by Support Vector Learning Approach
... of learning machines, which enable them to generalize well in the unseen data, whereas the fuzzy set theory might be very useful for finding a fuzzy structure in an evaluation ...proposed ... See full document
7
A Fuzzy Model of Support Vector Regression Machine
... a fuzzy system equation which can be described by the fuzzy functions defined by Zadeh’s extension ...of fuzzy set theory into the support vector machine ...SVM ... See full document
3
Travel Time Prediction with Support Vector Regression
... significantly reduces the prediction errors for the cases where there are worse prediction errors in any one of the predictors. VII. C ONCLUSION Support vector machine and SVR have demonstrated their ... See full document
6
Simple Probabilistic Predictions for Support Vector Regression
... simple approach for probabilistic prediction suitable for the standard ...Our approach starts with generating out-of-sample residuals by cross validation, and then fits the residuals by simple ... See full document
16
Multiclass support vector classification via coding and regression
... Classification by multiresponse regression Consider the problem of multiclass classification with J classes based on d measurements of input attributes x A R d1 ...set by J ¼ f1; 2; . . . ; Jg and ... See full document
12
Revising Traditional Technological Prediction Model by Support Vector Regression Tools 周祐徹、吳泰熙
... Siciliano,“Support vector machines for olfactory signals recognition ”,Sensors and Actuators B 88, ...“Support vector networks.”, Machine Learning 20, ...for support ... See full document
2
A new regularized least squares support vector regression for gene selection
... This approach allows the collected tissues to provide different strength of association with the ...data analysis show that the proposed selection procedure performs better in the sense that it attains a ... See full document
27
Stroke Prevention by traditional Chinese medicine A genetic algorithm, support vector machine and molecular dynamics approach
... screening by molecular ...machine learning and building QSAR model is becoming a routine process in drug design; some novel examples include using support vector machine (SVM) for identifying ... See full document
8
Toward the Best Feature Model for Network Intrusion Detection Using Stepwise Regression and Support Vector Machine
... linear regression (MLR) is rarely applied to anomaly-based network intrusion detection for feature ...our approach, we take a data-centric point of view and consider intrusion detection as a data ... See full document
6
The Prediction and Investitive Decision Software of Stock Price with Support Vector Machine and Fuzzy Theory
... with support vector regression machine and fuzzy decision is ...used support vector regression machine for Taiwan stock price that used daily and practical price to input ... See full document
6
Using Optimization Algorithms to Select Parameters of Support Vector Regression 楊舜麟、王正賢 ; 白炳豐
... Cooke, Learning using an artificial immune system, Journal of Network and Computer ...computing: support vector machines, neural networks, and fuzzy logic models”, Massachusetts Institute of ... See full document
2
Fuzzy Weighted Support Vector Regression Using the Dual Coordinate Descent Method
... 在無監督學習領域，聚類和混合回歸問題是我們非常感興趣的問題。因此，SVR 也被推 廣到混合回歸問題。基於模糊理論，延伸出了模糊加權支持向量迴歸(Fuzzy Weighted Support Vector Regression/FWSVR)。通過將隸屬度引入懲罰項，FWSVR 可以處理混合迴歸問題，而 不是像以前那樣一對一地使用 SVR。然而，FWSVR ... See full document
37
Support vector machines for data classification and regression
... Avoid underfitting : small training error Avoid overfitting : small testing error.. Overfitting[r] ... See full document
124
Fuzzy One-Class Support Vector Machines
... the support vector machine (SVM) methodology to the one-class classification problem, called one-class ...of fuzzy set theory into the one-class SVM. We apply a fuzzy membership to each input ... See full document
10
Training v-support vector regression: Theory and algorithms
... It can be clearly seen that for large ν, the decomposition method identifies the optimal face more quickly, so the total number of iterations is fewer. 4. When ν ≤ ν ∗ , we observe that there are minor differences in the ... See full document
19
A note on the decomposition methods for support vector regression
... Technical report, Department of Computer Science and Information Engineering, National Taiwan University, 2000. On the convergence of the decomposi- tion method for support vec[r] ... See full document
6
Active Learning with Hinted Support Vector Machine
... active learning, which is an important learning paradigm for reducing human labeling ...active learning algorithms consider both uncertainty and representative- ness when making querying ... See full document
15
Leave-one-out Bounds for Support Vector Regression Model Selection
... those by BSVR as it considers more than three parameters. Clearly these methods obtain quite distinct parameters even though they (except RM) give similar testing errors. This observation indicates that good ... See full document
34
One-sided Support Vector Regression for Multiclass Cost-sensitive Classification
... novel approach that reduces cost-sensitive classification to one-sided re- ...The approach stores the cost infor- mation in the regression labels and encodes the minimum-cost prediction with the one- ... See full document
8
Optimization, Support Vector Machines, and Machine Learning
... Many other optimization issues from machine learning Need to study things useful for ML tasks.. While we complain ML people’s lack of optimization knowledge, we must admit this fact firs[r] ... See full document
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