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此节课主要介绍了如下的概念:测度、Riemann integral与Lebesgue intergral的不同 近期希望补深数学，此笔记对应为台湾交通大学实分析课程。此课程为实分析的第一课， 此节课主要介绍了如下的概念:测度、Riemann integral与Lebesgue intergral的不同
Published in Journal 31, 2015
he resemblance between edible mushroom and poisonous mushroom in appearance makes it hard to distinguish them from each other by conventional methods. In order to achieve the automation of judgment and strengthen the reliability, this paper proposed a method to measure the toxicity of mushroom based on support vector machine. To begin with, collection and pre-processing of the sample data were conducted. Then C-SVM model was built up and trained in accordance with one-to-one principle to further achieve multiclassification by support vector machine. At last, constant step length method was applied to obtain the optimum parameters of the model. By comparing accuracy of SVM classification in diverse sample sizes and parameters, the feasibility was verified in simulation experiments. SVM was more accurate, easy-conducting and practical comparing with neural network and decision tree.
Recommended citation: Fan, Ge, et al. "Discriminant Method of Mushroom Toxicity Based on Support Vector Machine." Chinese Agricultural Science Bulletin 31(19):232-236, 2015. [PDF]
Published in Journal 1, 2018
Understanding user preference is essential to the optimization of recommender systems. As a feedback of user’s taste, the rating score can directly reflect the preference of a given user to a given product. Uncovering the latent components of user ratings is thus of significant importance for learning user interests. In this paper, a new recommendation approach was proposed by investigating the latent components of user ratings. The basic idea is to decompose an existing rating into several components via a cost-sensitive learning strategy. Specifically, each rating is assigned to several latent factor models and each model is updated according to its predictive errors. Afterward, these accumulated predictive errors of models are utilized to decompose a rating into several components, each of which is treated as an independent part to further retrain the latent factor models. Finally, all latent factor models are combined linearly to estimate predictive ratings for users. In contrast to existing methods, our method provides an intuitive preference modeling strategy via multiple component analysis at an individual perspective. Meanwhile, it is verified by the experimental results on several benchmark datasets that the proposed method is superior to the state-ofthe-art methods in terms of recommendation accuracy.
Recommended citation: Chen, Junhua, et al. "Preference modeling by exploiting latent components of ratings." Knowledge and Information Systems (2018): 1-27. [PDF]