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SVM and Its Related Applications

SVM and Its Related Applications

SVM and Its Related Applications. Jung-Ying Wang 5/24/2006. Outline. Basic concept of SVM SVC formulations Kernel function Model selection (tuning SVM hyperparameters) SVM application: breast cancer diagnosis Prediction of protein secondary structure

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698 views • 69 slides
Lecture 8: Evaluation

Lecture 8: Evaluation

Lecture 8: Evaluation. Machine Learning Queens College. Today. Evaluation Measures Accuracy Significance Testing Cross-validation F-Measure Error Types ROC Curves Equal Error Rate AIC/BIC. How do you know that you have a good classifier?.

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483 views • 37 slides
CS522 Advanced Database Systems Classification: Evaluation and Comparison of Classifiers

CS522 Advanced Database Systems Classification: Evaluation and Comparison of Classifiers

CS522 Advanced Database Systems Classification: Evaluation and Comparison of Classifiers. Chengyu Sun California State University, Los Angeles. Difficulties in Classifier Evaluation and Comparison. Training error is not a good indicator of testing error

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342 views • 34 slides
CLassification TESTING Testing classifier accuracy

CLassification TESTING Testing classifier accuracy

CLassification TESTING Testing classifier accuracy. Anita Wasilewska Lecture Notes on Learning. Reference. Student Presentation 2005: Zhiquan Gao Data Mining: Concepts and Techniques (Chapter 7), Jiawei Han and Micheline Kamber

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412 views • 25 slides
Artificial Neural Networks

Artificial Neural Networks

Artificial Neural Networks. Introduction Design of Primitive Units Perceptrons The Backpropagation Algorithm Advanced Topics. Basics. In contrast to perceptrons, multilayer networks can learn not only multiple decision boundaries, but the boundaries may be nonlinear. Output nodes.

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519 views • 22 slides
Validation

Validation

Validation. Introduction. Validation addresses the problem of over-fitting . Internal Validation: Validate your model on your current data set (cross-validation) External Validation: Validate your model on a completely new dataset. Holdout validation.

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386 views • 7 slides

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