Now showing items 1-7 of 7

    • AN EMPIRICAL STUDY OF THE SUITABILITY OF CLASS DECOMPOSITION FOR LINEAR CLASSIFIERS 

      Ocegueda-Hernandez, Francisco 1978- (2012-12)
      The presence of sub-classes within a data sample suggests a class decomposition approach to classification, where each subclass is treated as a new class. Class decomposition can be effected using multiple linear classifiers ...
    • Choosing the Right Kernel A Meta-Learning Approach to Kernel Selection in Support Vector Machines 

      Valerio Molina, Roberto 1983-; 0000-0002-4508-9788 (2015-05)
      In recent years Support Vector Machines (SVM) have gained increasing popularity over other classification algorithms due to their ability to produce a flexible boundary over non-linearly separable datasets. Such an ability ...
    • Computational Methods for Flood Forecasting 

      Hutapea, Christariny 1981- (2016-08-05)
      According to World Meteorological Organization (WMO), flooding is one of the most hazardous natural disasters, affecting millions of people globally every year. Over the years, many research have been conducted with the ...
    • DESIGN AND IMPLEMENTATION OF A CO-LOCATION ANALYSIS TOOL 

      Man, Xiaoxi 1984- (2014-12)
      In recent years the widespread usage of scanning device, such as GPS-enabled devices, PDAs, and video cameras, has resulted in an abundance of spatial data. Therefore, there is an increasing interest in mining hidden ...
    • INFORMATION GAIN FEATURE SELECTION BASED ON FEATURE INTERACTIONS 

      Sui, Bangsheng 1987- (2013-12)
      Analyzing high-dimensional data stands as a great challenge in machine learning. In order to deal with the curse of dimensionality, many effective and efficient feature-selection algorithms have been developed recently. ...
    • Spatial and Spatio-Temporal Clustering 

      Wang, Sujing 1975- (2014-05)
      Due to the advances in technology, such as smart phones, general mobile devices, remote sensors, and sensor networks, different types of spatial data become increasingly available. These data can also integrate multiple ...
    • Using Machine Learning for Automatic Classification of Classical Cepheids 

      Kidd, Dallas 1988-; 0000-0003-2079-4752 (2015-05)
      With the increasing amounts of astronomical data being gathered, it is becoming more crucial for machine learning techniques to be employed for star classification. Classical Cepheid variable stars can be grouped into ...