NDLI: An updating method for support vector learning

Content Provider	IEEE Xplore Digital Library
Author	Yang-Guang Liu Qin-Ming He Qi Chen
Copyright Year	2005
Description	Author affiliation: Inst. of Artificial Intelligence, Zhejiang Univ., China (Yang-Guang Liu; Qin-Ming He; Qi Chen)
Abstract	Learning is obtaining an underlying rule by using training data sampled from the environment. In many practical situations in inductive learning algorithms, it is often expected to further improve the generalization capability after the learning process has been completed if new data are available. One of the common approaches is to add training data to the learning algorithm and retrain it, but retraining for each new data point or data set can be very expensive. In view of the learning methods of human beings, it seems natural to build posterior learning results upon prior results. In this paper, we proposed an updating method for support vector machine (SVM). If initial concept would be built up by inductive algorithm, then concept updated is the normal solution corresponding to the initial concept learned. It was shown that concept learned would not change if the new available data located in error-insensitive zone. Especially, concept initially learned and updated by SVR induces an incremental SVR approximately learning method for large scale data. We tested our method on toys data sets and 7 regression bench mark data set. It is shown that generalization capacity after updating with SVR was improved according to FVU or MSE on the independent test set.
Sponsorship	IEEE Syst., Man and Cybernetics Tech. Comm. on Cybernetics, Hong Kong Polytechnic Univ. Hebei Univ. South China Univ. Chongqing Univ. Sun Yat-sen Univ. Harbin Inst. of Technol. and Int. Univ. in Germany
Starting Page	3109
Ending Page	3114
File Size	322129
Page Count	6
File Format	PDF
ISBN	0780390911
DOI	10.1109/ICMLC.2005.1527476
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-08-18
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Training data Learning systems Support vector machine classification Humans Large-scale systems Testing Machine learning Helium Artificial intelligence learning Support Vector Machine concept updating method regression
Content Type	Text
Resource Type	Article

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