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groupnews:seminar_on_local_global_and_hybrid_learning_at_institute_of_information_science_academia_sinica_taiwan_on_november_4_2005 [2005/11/05 00:00] (current) |
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| + | ====== Seminar on "Local, Global, and Hybrid Learning" at Institute of Information Science, Academia Sinica, Taiwan on November 4, 2005... ====== | ||
| + | |||
| + | The use of classifiers permeates in various fields of | ||
| + | engineering and science disciplines. When constructing | ||
| + | classifiers, there is a dichotomy in choosing whether to | ||
| + | use local vs. global characteristics of the input data. In | ||
| + | this talk, we will describe our work on combining the local | ||
| + | and global learning in the Maxi-Min Margin Machine (M^4). | ||
| + | M^4 presents a unifying theory that subsumes the Support | ||
| + | Vector Machine (SVM), the Minimax Probability Machine | ||
| + | (MPM), and the Linear Discriminant Analysis (LDA). While | ||
| + | LDA and MPM focus on building the decision plane using | ||
| + | global information and SVM focuses on building the decision | ||
| + | plane in a local manner, M^4 incorporates these two | ||
| + | seemingly different yet complementary characteristics in a | ||
| + | unifying framework that achieves good classification | ||
| + | accuracy. We will present the formulation of M^4 and also | ||
| + | experimental results to show the advantage of our novel | ||
| + | model. | ||
| + | |||
| + | * [[http://www.iis.sinica.edu.tw/Function/seminar-new.php?TYPE=SE]] | ||
| + | * [[http://www.iis.sinica.edu.tw/HTML/ENGLISH/S200511041000.html]] | ||
| + | |||
| + | ~~DISCUSSION~~ | ||
