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Home> Schools> University of Wisconsin, Madison> Electrical and Computer Engr (ECE) > ECE 539> Forest Cover Type Prediction

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UW-Madison ECE 539 - Forest Cover Type Prediction

School name University of Wisconsin, Madison

Course Ece 539- Introduction to Artificial Neural Network and Fuzzy Systems

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Forest Cover Type PredictionOutlineProblem StatementPractical ConsiderationsResultsForest Cover Type PredictionECE 539 Term ProjectBrett Meyer12/14/01Problem StatementPractical ConsiderationsResultsOutlineUse a SVM to correctly distinguish between seven forest cover typesPreviously, 70% achieved using a MLPProblem StatementLarge scale classification problem580,000 data points, 54 columns of inputMany new issues arise when using large filesSeven data classesUneven distributionTwo classes make up 85% of data pointsOne class contributes only 0.5% of data pointsPractical ConsiderationsBest to date: 70% classificationAttainable using radial basis SVMVerified in multiple trialsStandard deviation:

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UW-Madison ECE 539 - Forest Cover Type Prediction

School: University of Wisconsin, Madison

Course: Ece 539- Introduction to Artificial Neural Network and Fuzzy Systems

Pages: 5

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