Knowledge reduction in decision table is important in both theory and application, and it outputs a minimal algorithm as a result. Set of the samples fitting the minimal algorithm is a concept over the set of all poss...
详细信息
Knowledge reduction in decision table is important in both theory and application, and it outputs a minimal algorithm as a result. Set of the samples fitting the minimal algorithm is a concept over the set of all possible instances. But in unfamiliar environment, decision table is obtained randomly. So the obtained concept is an approximation to a potential target concept. We discuss the model of this concept learning, sample complexity of its hypothesis space and PAC-learnability of its target concept class.
Feature representation and feature fusion are important factors in image classification problem. In this paper, the local features, mid-level features and convolutional features are combined using the multiple kernel ...
Feature representation and feature fusion are important factors in image classification problem. In this paper, the local features, mid-level features and convolutional features are combined using the multiple kernel learning method. Experimental results show that the local features, mid-level features and convolutional features can be fused effectively to improve the classification performance about 4%-6% on several popular benchmarks.
Feature selection is an essential technique used in data mining and machinelearning. Many feature selection methods have been studied for supervised problems. However feature selection for unsupervised learning is ra...
详细信息
Feature selection is an essential technique used in data mining and machinelearning. Many feature selection methods have been studied for supervised problems. However feature selection for unsupervised learning is rarely studied. In this paper, we proposed an approach to select features for unsupervised problems. Firstly, the original features are clustered according to their relevance degree defined by mutual information. And then the most informative feature is selected from each cluster based on the contribution-information of each feature. The experimental results show that the proposed method can match some popular supervised feature selection methods. And the features selected by our method do include most of the information hidden in the overall original features.
Feature weighting, which is considered as an extension of feature selection techniques, has been successfully applied to improve the performance of clustering. Focusing on the clustering based on a similarity matrix, ...
详细信息
ISBN:
(纸本)9781479902590
Feature weighting, which is considered as an extension of feature selection techniques, has been successfully applied to improve the performance of clustering. Focusing on the clustering based on a similarity matrix, we design an optimization model to minimize the fuzziness of similarity matrix by learning feature weights. The objective of this model is to get a more reasonable result of clustering through minimizing the uncertainty (fuzziness and non-specificity) of similarity matrix. To solving this optimization model effectively, we propose a new searching approach which integrates together multiple evolution strategies of both differential evolution and dynamic differential evolution. The experimental results on several benchmark datasets show that the performance of the proposed method is significantly improved compared to that of gradient-descent-based approach in terms of five selected clustering evaluation indices, i.e., fuzziness of similarity matrix, intra-class similarity, inter-class similarity, ratio of intra-class similarity to inter-class similarity.
In Chinese-chess computer game (CCCG), a computer player could find the best move for a given board position by using alpha-beta search algorithm. The technique of iterative deepening is an enhancement to alpha-beta s...
详细信息
In Chinese-chess computer game (CCCG), a computer player could find the best move for a given board position by using alpha-beta search algorithm. The technique of iterative deepening is an enhancement to alpha-beta search. It is helpful to reduce the size of game tree. In this paper, we improved the prototypical one-ply iterative deepening (OPID) and proposed two-ply iterative deepening (TPID). In game tree searching, we extend the search by two plies from the previous iteration. An iterated series of 2-ply, 4-ply, 6-ply,…searches is carried out. In the experiments, we validate that TPID is feasible and effective. Through applying TPID to minimax search and alpha-beta search respectively, we found that the total number of nodes generated in TPID minimax search and TPID alpha-beta search are all reduced compared with OPID.
Determining fuzzy measure from data is an important topic in some practical applications. Some computing techniques are adopted, such as particle swarm optimization (PSO) and gradient descent algorithm (GD), to identi...
详细信息
Determining fuzzy measure from data is an important topic in some practical applications. Some computing techniques are adopted, such as particle swarm optimization (PSO) and gradient descent algorithm (GD), to identify fuzzy measure. However, there exist some limitations. In this paper, we design a hybrid algorithm called CDPSO, through introducing GD to PSO for the first time. This algorithm has the advantages of GD and PSO, and avoids the disadvantages of them. Theoretical analysis and experimental results verify this, and show that GDPSO is effective and efficient.
Searching frequent patterns in transactional databases is considered as one of the most important data mining problems and Apriori is one of the typical algorithms for this task. Developing fast and efficient algorith...
详细信息
Classification based on association rules is a common and easily understand algorithm for text classification. To improve its classification accuracy, the key is to generate more effective rules. Sometimes, it will ov...
详细信息
Support Vector machine (SVM) is sensitive to noises and outliers. For reducing the effect of noises and outliers, we propose a novel SVM for suppressing error function. The error function is limited to the interval of...
详细信息
ISBN:
(纸本)9780769538877
Support Vector machine (SVM) is sensitive to noises and outliers. For reducing the effect of noises and outliers, we propose a novel SVM for suppressing error function. The error function is limited to the interval of [0, 1]. The separation hypersurface is simplified and the margin of hypersurface is widened. Experimental results show that our proposed method is able to simultaneously increase the classification efficiency and the generalization ability of the SVM.
Markov chains, with Markov property as its essence, are widely used in the fields such as information theory, automatic control, communication techniques, genetics, computer sciences, economic administration, educatio...
详细信息
Markov chains, with Markov property as its essence, are widely used in the fields such as information theory, automatic control, communication techniques, genetics, computer sciences, economic administration, education administration, and market forecasts. While using Markov chains to predict the future events, we must test the Markov property of random variable sequences of the past statistic data. Only when the random variable sequences satisfy the Markov property, can the prediction could be precise. This paper discusses the concept of Markov property and its features, studies its test method, and by example demonstrates the effectiveness of this prediction method.
暂无评论