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Pattern Recognition and Machine Learning

Pattern Recognition and Machine Learning

Pattern Recognition and Machine Learning. Chapter 1: Introduction. Example. Handwritten Digit Recognition. Polynomial Curve Fitting . Sum-of-Squares Error Function. 0 th Order Polynomial. 1 st Order Polynomial. 3 rd Order Polynomial. 9 th Order Polynomial. Over-fitting.

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916 views • 59 slides
Pattern Recognition and Machine Learning

Pattern Recognition and Machine Learning

Pattern Recognition and Machine Learning. Chapter 1: Introduction. Example. Handwritten Digit Recognition. Polynomial Curve Fitting . Sum-of-Squares Error Function. 0 th Order Polynomial. 1 st Order Polynomial. 3 rd Order Polynomial. 9 th Order Polynomial. Over-fitting.

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1.57k views • 59 slides
L ecture 2: Overview of Supervised Learning

L ecture 2: Overview of Supervised Learning

L ecture 2: Overview of Supervised Learning. Outline. Regression vs. Classification Two Basic Ideas: Linear Least Square -> generalized linear models Nearest Neighbors -> kernel method and SVM Bias- Variance Decomposition C urse of Dimensionality and M odel Selection

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464 views • 46 slides
L ecture 2: Overview of Supervised Learning

L ecture 2: Overview of Supervised Learning

L ecture 2: Overview of Supervised Learning. Outline. Regression vs. Classification Two Basic Methods: Linear Least Square vs. Nearest Neighbors C lassification via Regression C urse of Dimensionality and M odel Selection G eneralized Linear Models and Basis Expansion.

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453 views • 45 slides
Class Imbalance vs. Cost-Sensitive Learning

Class Imbalance vs. Cost-Sensitive Learning

Class Imbalance vs. Cost-Sensitive Learning . Presenter: Hui Li University of Ottawa. Contents. Introduction Making Classifier Balanced Making Classifier Cost Sensitive MetaCost Stratification. Class Imbalance vs. Asymmetric Misclassification costs.

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733 views • 43 slides

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