In this paper we present a k-means transition ranking (KMTR) framework to solve the MKP. This framework has the property to binarize continuous population-based metaheuristics using a data mining k-means techni
The goal is to develop a framework what, along with any adaptive thresholding method and a few a priori parameters, is able to extract the text data as binarized output. The input image can be in color or multi-spectral. As the first step in making that image compatible with most ...
interpretation algorithms Machine Perception (speech recognition, computer vision, ...). We therefore propose to investigate formal data mining techniques to try and measure levels of agreement and disagreement between various algorithms. Our application domain will be in the area of Document Image ...
This requires dedicated machine learning solutions that are able to handle various difficulties embedded in the nature of data. In this paper, we present an efficient framework for automatic sentiment analysis from high-dimensional and sparse datasets that suffer from multi-class imbalance. We propose...
Mayoraz, E., Moreira, M.: Combinatorial approach for data binarization. In: Principles of Data Mining and Knowledge Discovery, vol. 1704 of the Series Lecture Notes in Computer Science, pp. 442-447 (1999)Mayoraz, E., & Moreira, M. (1999). Combinatorial approach for data binarization. ...
Knowledge Discovery and Data MiningChun Che Fung, R. Chamchong, A Review of Evaluation of Optimal Binarization Technique for Character Segmentation in Historical Manuscripts, in: IEEE, 2010: pp. 236-240. doi:10.1109/WKDD.2010.110.C.C. Fung, R. Chamchong, "A Review of Evaluation of Optimal...
Jose Garcia, Broderick Crawford, Ricardo Soto, and Gino Astorga. A percentile transition ranking algorithm applied to binarization of continuous swarm intelli- gence metaheuristics. In International Conference on Soft Computing and Data Mining, pages 313. Springer, 2018....
Technological advances and the digitization of information have allowed us to obtain a large amount of data from different processes such as medicine, commerce, and mining, among others. All this data has been used as input by different researchers in machine learning techniques to speed up the ...
Won NahJoong-Hwan BaekRough sets, fuzzy sets, data mining, and granular computing: 9th international conference on rough sets, fuzzy sets, data mining, and granular computing(RSFDGrC 2003), May 26-29, 2003, Chongqing, China
Then, it is recorded that agents went through states, chose actions, and acquired rewards in an episode, and then this information will be used to update the Q-function again. When the agent reaches the target state in the current episode, the produced data is used to update the Q-...