The Experts below are selected from a list of 57957 Experts worldwide ranked by ideXlab platform
Keiki Takadama - One of the best experts on this subject based on the ideXlab platform.
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comparison of Statistical Table and non Statistical Table based xcs in noisy environments
Congress on Evolutionary Computation, 2019Co-Authors: Takato Tatsumi, Keiki TakadamaAbstract:Accuracy based Learning Classifier System (XCS) acquires generalized classifiers that can guess the appropriate output for all inputs with a small number of the classifiers in ideal environments where there is no uncertainty in inputs, outputs, and rewards. However, if uncertainty is included in any of inputs, outputs, and rewards, XCS cannot be properly learned and cannot stably acquire generalized classifiers. We proposed Learning Classifier Systems that can properly learn in environments to which specific noise is added. These methods are divided into two types: (i) Statistical Table based XCS that record the mean of rewards acquired in all input-output pairs, and (ii) non-Statistical Table based XCS that do not record their values. This paper applies these methods to multiple noise environments and clarifies the features of each method.
Gary Marchionini - One of the best experts on this subject based on the ideXlab platform.
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free link topology navigation on Statistical Table objects metadata schema and user interface
International Conference on Digital Government Research, 2003Co-Authors: Gary MarchioniniAbstract:A Table object, defined and described in XML, is treated as the basic unit in a free-link topology that enables multiple inheritances for each node. In this paper, a novel Table navigation system called TableHunter is introduced. Each Table object is presented in the TableHunter as a node of a map structure, and supports a context+focus view.
Takato Tatsumi - One of the best experts on this subject based on the ideXlab platform.
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comparison of Statistical Table and non Statistical Table based xcs in noisy environments
Congress on Evolutionary Computation, 2019Co-Authors: Takato Tatsumi, Keiki TakadamaAbstract:Accuracy based Learning Classifier System (XCS) acquires generalized classifiers that can guess the appropriate output for all inputs with a small number of the classifiers in ideal environments where there is no uncertainty in inputs, outputs, and rewards. However, if uncertainty is included in any of inputs, outputs, and rewards, XCS cannot be properly learned and cannot stably acquire generalized classifiers. We proposed Learning Classifier Systems that can properly learn in environments to which specific noise is added. These methods are divided into two types: (i) Statistical Table based XCS that record the mean of rewards acquired in all input-output pairs, and (ii) non-Statistical Table based XCS that do not record their values. This paper applies these methods to multiple noise environments and clarifies the features of each method.
Quanqiu Wang - One of the best experts on this subject based on the ideXlab platform.
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combining automatic Table classification and relationship extraction in extracting anticancer drug side effect pairs from full text articles
Journal of Biomedical Informatics, 2015Co-Authors: Quanqiu WangAbstract:Display Omitted Cancer drugs are often associated high toxicities.There exists no comprehensive knowledge base of cancer drug toxicities.Systematic studies of cancer drug-associated toxicities can facilitate drug discovery.We developed an integrated approach to extract drug-SE pairs from full-text oncological articles. Anticancer drug-associated side effect knowledge often exists in multiple heterogeneous and complementary data sources. A comprehensive anticancer drug-side effect (drug-SE) relationship knowledge base is important for computation-based drug target discovery, drug toxicity predication and drug repositioning. In this study, we present a two-step approach by combining Table classification and relationship extraction to extract drug-SE pairs from a large number of high-profile oncological full-text articles. The data consists of 31,255 Tables downloaded from the Journal of Oncology (JCO). We first trained a Statistical classifier to classify Tables into SE-related and -unrelated categories. We then extracted drug-SE pairs from SE-related Tables. We compared drug side effect knowledge extracted from JCO Tables to that derived from FDA drug labels. Finally, we systematically analyzed relationships between anti-cancer drug-associated side effects and drug-associated gene targets, metabolism genes, and disease indications. The Statistical Table classifier is effective in classifying Tables into SE-related and -unrelated (precision: 0.711; recall: 0.941; F1: 0.810). We extracted a total of 26,918 drug-SE pairs from SE-related Tables with a precision of 0.605, a recall of 0.460, and a F1 of 0.520. Drug-SE pairs extracted from JCO Tables is largely complementary to those derived from FDA drug labels; as many as 84.7% of the pairs extracted from JCO Tables have not been included a side effect database constructed from FDA drug labels. Side effects associated with anticancer drugs positively correlate with drug target genes, drug metabolism genes, and disease indications.
Hiroyuki Kawashima - One of the best experts on this subject based on the ideXlab platform.
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estimating total area of paddy fields in heilongjiang china around 2000 using landsat thematic mapper enhanced thematic mapper plus data
Remote Sensing Letters, 2016Co-Authors: Katsuo Okamoto, Hiroyuki KawashimaAbstract:ABSTRACTAgricultural statistics are a fundamental reference for evaluating damage caused by natural disasters, estimating food supply and demand, and framing policies. A Statistical Table is usually prepared by an administrative district. Unfortunately, the Heilongjiang Statistical Yearbook of China was not completely prepared by such a district. Therefore, remote sensing technology is necessary for estimating the total area of agricultural lands in each administrative district. The test area is the Heilongjiang Province in China. The Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) data acquired during and immediately after the rice-planting season around 2000 (1999–2002) were used for the land-use/land-cover classification. All possible data during or immediately after the rice-planting season (from the beginning of June to the beginning of July) were selected so that paddy fields could be detected accurately. Borders of prefecture-level cities were generated using borders of cities...