By Kai-Zhu Huang, Haiqin Yang, Michael R. Lyu
Machine studying - Modeling information in the neighborhood and Globally offers a unique and unified conception that attempts to seamlessly combine diverse algorithms. particularly, the e-book distinguishes the internal nature of desktop studying algorithms as both "local learning"or "global learning."This thought not just connects earlier computer studying tools, or serves as roadmap in quite a few types, yet – extra importantly – it additionally motivates a idea that could examine from info either in the neighborhood and globally. this is able to support the researchers achieve a deeper perception and entire knowing of the concepts during this box. The booklet studies present topics,new theories and applications.
Kaizhu Huang was once a researcher on the Fujitsu study and improvement middle and is at present a learn fellow within the chinese language collage of Hong Kong. Haiqin Yang leads the picture processing team at HiSilicon applied sciences. Irwin King and Michael R. Lyu are professors on the laptop technology and Engineering division of the chinese language collage of Hong Kong.
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Additional info for Machine Learning: Modeling Data Locally and Globally (Advanced Topics in Science and Technology in China)
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22 2 Global Learning vs. 3 Local Learning Local learning adopts a largely different way to construct classifiers. This type of learning is even more task-oriented than Minimum Error Minimax Probability Machine and Maximal Conditional learning. In the context of classifications, only the final mapping function from the features z to c is crucial. Therefore, describing global information from data or explicitly summarizing a distribution whatever is conditional or joint, is a roundabout or intermediate step and therefore may be deemed wasteful or imprecise especially when the global information cannot be estimated accurately.
In Proceedings of IEEE International Conference on Systems, Man and Cybernetics (SMC2002). Hammamet, Tunisia TA1F3 21. Huang K, King I, Lyu MR (2003) Finite mixture model of bound semi-naive Bayesian network classifier. In Proceedings of the International Conference on Artificial Neural Networks (ICANN-2003), Lecture Notes in Artificial Intelligence, Long Paper. Heidelberg: Springer-Verlag 2714: 115–122 22. Jebara T (2002) Discriminative, Generative and Imitative Learning. PhD thesis, Massachusetts Institute of Technology 23.
Furthermore, by exploring the bound, the recentlyproposed promising model, Minimax Probability Machine is clearly demonstrated to be its special case. Importantly, based on specifying a bound for one class of data, a Biased Minimax Probability Machine is branched out from MEMPM, which will be shown to provide a rigorous and systematic treatment for biased classifications. We will detail the MEMPM model and BMPM model in the next chapter. 22 2 Global Learning vs. 3 Local Learning Local learning adopts a largely different way to construct classifiers.