By Dr. Kaizhu Huang, Dr. Haiqin Yang, Prof. Irwin King, Dr. Michael Lyu (auth.)
Machine studying - Modeling info in the community and Globally offers a singular and unified conception that attempts to seamlessly combine varied algorithms. in particular, the ebook distinguishes the interior nature of laptop studying algorithms as both "local learning"or "global learning."This concept not just connects earlier computer studying equipment, or serves as roadmap in numerous types, yet – extra importantly – it additionally motivates a conception which may examine from facts either in the community and globally. this might support the researchers achieve a deeper perception and accomplished realizing of the options during this box. The ebook stories present topics,new theories and applications.
Kaizhu Huang was once a researcher on the Fujitsu learn and improvement heart and is presently a examine fellow within the chinese language college 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 technological know-how and Engineering division of the chinese language college of Hong Kong.
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Additional info for Machine Learning: Modeling Data Locally and Globally
In other words, the optimal w lies in the vector space spanned by all the training data points. Note that the introduction of ρx and ρy actually enables a direct application of the robust estimates in the kernelization. 8, if appropriate estimates of means and covariance matrices are applied, the optimal w can be written as the linear combination of training points. e. 43) j=1 where the coeﬃcients μi , υj ∈ R for i = 1, . . , Nx and j = 1, . . , Ny . 42), we can obtain the Kernelization Theorem of BMPM.
However, it is widely argued that this model lacks the generality for having to assume a speciﬁc model beforehand. Assuming a speciﬁc model over data is useful in some cases. However, the assumption may not always coincide with the true data distribution in general and thus may be invalid in many circumstances. In this chapter, we propose a novel global learning model, named Minimum Error Minimax Probability Machine (MEMPM), which is directly motivated from Marshall and OlKin Probability Theory [20, 24].
Another important ﬁnding is that the accuracy bounds, namely θα + (1 − θ)β in MEMPM and α in MPM are all increased in the Gaussian kernel setting when compared with those in the linear setting. This shows the advantage of the kernelized probability machine over the linear probability machine. In addition, to clearly see the relationship between the bounds and the test set accuracies (T SA), we plot them in Fig. 5. As observed, the test set accuracies including T SAx (for the class x), T SAy (for the class y), and the overall accuracies T SA are all greater than their corresponding accuracy bounds both in MPM and MEMPM.