Now showing items 1-6 of 6

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    A filter-based feature selection using two criterion functions and evolutionary fuzzification 

    Jitwadee Chaiyakarn; Ohm Sornil (National Institute of Development Administration, 2013)

    In information age, data has become increasingly large, in both dimension (the number of features) and volume. Data mining processes, such as data classification and data clustering, performed on high dimensional data can be time-consuming and can produce poor results due to the problem so called curse of dimensionality. Feature selection is one of the fundamental techniques that selects only the most significant features and eliminates irrelevant and redundant features from the entire set of features. Filter-based feature selection is the ...
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    Abstractive Thai opinion summarization 

    Orawan Chaowalit; Ohm Sornil (National Institute of Development Administration, 2013)

    With the advancement of the Internet technology, customers can easily share opinions about services and products in forms of reviews. There can be large amount of reviews for popular products. Manually summarizing those reviews for important issues is a daunting task. Automatic opinion summarization is a solution to the problem. The task is more complicated for reviews written in Thai. Thai words are written continuously without space, and there is no symbol to identify the end of a sentence. Many reviews are written informally, thus accurate ...
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    Collaborative resource discovery in mobile peer-to-peer networks 

    Ajay Arunachalam; Ohm Sornil (National Institute of Development Administration, 2015)

    Resource discovery is the process to find the queried object also referred as resource searching. With the rapid development of mobile hardware and wireless internet access, it became feasible to use mobile devices in P2P network. Deploying unstructured Peer-to-Peer (P2P) applications over Mobile Ad-hoc Network (MANET) has seen a decade effort, but still has many limitations and challenges. Searching in such resultant networks are not efficient and further ineffective mainly due to mobility, peer discovery and connectivity issues. Typical resource ...
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    Feature selection using genetic algorithm 

    Kanyanut Homsapaya; Ohm Sornil (สถาบันบัณพิตพัฒนบริหารศาสตร์, 2017)

    In this dissertation, a method of feature selection in machine learning, and more particularly supervised learning is presented. Supervised learning is a machine learning task that infers answers from a training data set. In machine learning, training datasets are employed in order to create a model which enables reasonable predictions, while in supervised learning, each training example is a training set consisting of instances and labels, and the learning objective is to be able to predict the label of a new unseen instance with as few ...
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    Thai soundex using Thai phonetic distance algorithm 

    Chonlasith Jucksriporn; Ohm Sornil (National Institute of Development Administration, 2014)

    Homophones are words with similar sound. Searching for homophones is not only looking for the words with similar spelling, but it should also be looking for the words with similar pronunciation. Specifically in Thai, some consonant clusters can be pronounced in different ways, such as // can be pronounced as // or // depending on cach particular word. This makes Thoi word pronunciation and dictation more dilliculi Thai sounder could not handle these consonant clusters properly: for example, many Thai soundex methods showed cncoded results of a ...
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    Using conceptual space and cultural evolution in language game 

    Theerapol Limsatta; Ohm Sornil (National Institute of Development Administration, 2016)

    Agreement on word-object pairing in communication depends on the intensity of belief that gradually emerges in a society of agents under the condition that no one is born with embedded knowledge. In know-nothing word-object pairing, the agents in communication find meaning until they reach a consensus on what an object should be called. A language game is a social process of finding agreement on word-object pairing which enables its communication in a multi-agent system. In this research, techniques are proposed to discover the association ...