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Data Classification Using Various Learning Algorithms
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ABSRACT - [ Total Page(s): 1 ]Dimensionality reduction provides a compact representation of an original high-dimensional data, which means the reduced data is free from any further processing and only the vital information is retained. For this reason, it is an invaluable preprocessing step before the application of many machine learning algorithms that perform poorly on high-dimensional data. In this thesis, the perceptron classification algorithm – an eager learner - is applied to three two-class datasets (Student, Weather and Ionosphere datasets). The k-Nearest Neighbors classification algorithm - a lazy learner
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CHAPTER ONE - [ Total Page(s): 2 ]INTRODUCTION1.1 Background of the StudyData volumes and variety are increasing at an alarming rate making very tedious any attempt to glean useful information from these large data sets. Extracting or mining useful information and hidden patterns from the data is becoming more and more important but can be very challenging at the same time [1]. A lot of research done in domains like Biology, Astronomy, Engineering, Consumer Transactions and Agriculture, deal with extensive sets of observations daily. Traditional statistical techniques encounter some challenges in analyzing these datasets due t ... Continue Reading
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ABSRACT - [ Total Page(s): 1 ]Dimensionality reduction provides a compact representation of an original high-dimensional data, which means the reduced data is free from any further processing and only the vital information is retained. For this reason, it is an invaluable preprocessing step before the application of many machine learning algorithms that perform poorly on high-dimensional data. In this thesis, the perceptron classification algorithm – an eager learner - is applied to three two-class datasets (Student, Weather and Ionosphere datasets). The k-Nearest Neighbors classification algorithm - a lazy learner
... Continue Reading
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