Now showing items 1-4 of 4

    • Effective and Efficient Optimization Methods for Kernel Based Classification Problems 

      Tayal, Aditya (University of Waterloo, 2014-04-22)
      Kernel methods are a popular choice in solving a number of problems in statistical machine learning. In this thesis, we propose new methods for two important kernel based classification problems: 1) learning from highly ...
    • Exemplar-based Kernel Preserving Embedding 

      Elbagoury, Ahmed (University of Waterloo, 2016-05-02)
      With the rapid increase of available data, it becomes computationally harder to extract useful information, specially in the case of high-dimensional data. Choosing a representative subset of the data can be useful to ...
    • The Power Landmark Vector Learning Framework 

      Xiang, Shuo (University of Waterloo, 2008-05-12)
      Kernel methods have recently become popular in bioinformatics machine learning. Kernel methods allow linear algorithms to be applied to non-linear learning situations. By using kernels, non-linear learning problems can ...
    • Scalable Embeddings for Kernel Clustering on MapReduce 

      Elgohary, Ahmed (University of Waterloo, 2014-02-18)
      There is an increasing demand from businesses and industries to make the best use of their data. Clustering is a powerful tool for discovering natural groupings in data. The k-means algorithm is the most commonly-used data ...


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