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In this paper, we address the Bayesian classification with incomplete data. The common approach in the literature is to simply ignore the samples with missing values or impute missing values before classification. However, these methods are not effective when a large portion of the data have missing values and the acquisition of samples is expensive.(More)
In this paper, we develop a model-free and data efficient batch reinforcement learning algorithm for learning control of continuous state-space and discounted-reward Markov decision processes. This algorithm is an approximate value iteration which uses the manifold regularization method to learn feature representations for Q-value function approximation.(More)
Many areas in China, e.g., Beijing-Tianjin-Hebei area, have been suffering from severe air pollution problem in recent years. The haze has jeopardized people's health and aroused deep concern from the public. To understand residents concerns better and leverage the power of the crowd, the government encourages organizations and individuals to participate in(More)
This study presents a novel approach to unsupervised learning for clustering with missing data. We first extend a finite mixture model to the infinite case by considering Dirichlet process mixtures, which can automatically determine the number of mixture components or clusters. Furthermore, we view the missing features as latent variables and compute the(More)
Optimizing the process parameters is recognized as one of the most important steps to reduce the manufacturing variance. In this paper, we proposed a collaborative filtering (CF) algorithm in which the process parameters are optimized referring to the most similar well-controlled historical records in an environment-sensitive process manufacturing. A(More)
This paper studies the portfolio selection problem under the fuzzy environment. First, we introduce the concept of CVaR of fuzzy variable, and then under this concept a fuzzy mean-CVaR model is proposed. In general it is impossible to find out the closed form solution, thus a hybrid intelligent algorithm is presented. Finally, an example is provided to(More)
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