In this paper a supervised topic model is proposed for rank learning. The original supervised topic model can only learn from positive samples. For rank learning problem, training data have different ranking labels. To solve this issue, we extend the supervised topic model and make it learn from training data with different ranking labels. The experiments show that the proposed topic models can fi...Show More
Traditional evolutionary algorithms require a lot of memory and processing power on embedded logic projects. Representing populations of candidate solutions through vectors of probabilities rather than sets of bit strings saves memory and processing. The concise evolutionary algorithm (CEA) is a probability vector based evolutionary algorithm. The article presents an FPEA realization of the standa...Show More
