Titlebook: Uncertainty Modeling for Data Mining; A Label Semantics Ap Zengchang Qin,Yongchuan Tang Book 2014 The Editor(s) (if applicable) and The Aut

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Graves’-disease

Introduction,ull of uncertainties. Uncertainty is an objective and undeniable fact of nature. The second stream implies that the nature is governed by orders and laws. However, we cannot perceive all these laws from our limited cognitive abilities. That is where the uncertainties come from. The existence of unce

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Induction and Learning, than what appears to be present in the information to which they have been exposed. Chomsky referred to it as Plato’s problem to describe the gap between knowledge and experience.. Induction can be regarded as an important property of intelligence. Human beings have the ability of generalizing from

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询问

Linguistic Decision Trees for Classification,d where nodes are linguistic descriptions of variables and leaves are sets of appropriate labels. For each branch, instead of labeling it with a certain class, the probability of a particular class given this branch can be computed based on the given training dataset. This new model is referred to a

Memorial

Linguistic Decision Trees for Prediction,ed to a number of well known classifiers such as Naive Bayes and BP-neural networks. In this chapter, a methodology for extending LDTs to prediction problem is proposed and the performance of LDTs are compared to other state of the art prediction algorithms such as a Support Vector Regression (SVR)

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Bayesian Methods Based on Label Semantics,wever, for some complex problems, good probability estimations can only be obtained by deep LDTs, which have low transparency. In such cases, how can we build a model which has a good probability estimation but which uses compact LDTs? In this chapter, two hybrid learning models are proposed combini

精密

Unsupervised Learning with Label Semantics,hed from supervised learning, semi-supervised learning and reinforcement learning in that the learner is given only unlabeled data. Unsupervised learning is closely related to the problem of density estimation in statistics. However unsupervised learning also encompasses many other techniques that s

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