'0 1 loss function' diaporamas de présentation

Support Vector Machines and Radial Basis Function Networks

Support Vector Machines and Radial Basis Function Networks. Instructor: Tai-Yue (Jason) Wang Department of Industrial and Information Management Institute of Information Management. Statistical Learning Theory. Learning from Examples. Learning from examples is natural in human beings

2.25k views • 194 slides

Support Vector Machines and Radial Basis Function Networks

Support Vector Machines and Radial Basis Function Networks. Instructor: Tai-Yue (Jason) Wang Department of Industrial and Information Management Institute of Information Management. Statistical Learning Theory. Learning from Examples. Learning from examples is natural in human beings

2.16k views • 194 slides

Loss-based Learning with Weak Supervision

Loss-based Learning with Weak Supervision. M. Pawan Kumar. Computer Vision Data. Log (Size). ~ 2000. Segmentation. Information. Computer Vision Data. ~ 1 M. Log (Size). Bounding Box. ~ 2000. Segmentation. Information. Computer Vision Data. > 14 M. ~ 1 M. Log (Size). Image-Level.

1.54k views • 142 slides

Discriminative Machine Learning Topic 2: Linear Classification (SVM)

1.2k views • 120 slides

Digital Image Processing Lecture 25: Object Recognition

Digital Image Processing Lecture 25: Object Recognition. Prof. Charlene Tsai. Review. Matching Specified by the mean vector of each class Optimum statistical classifiers Probabilistic approach Bayes classifier for Gaussian pattern classes

320 views • 17 slides

Digital Image Processing Lecture 25: Object Recognition June 15, 2005

Digital Image Processing Lecture 25: Object Recognition June 15, 2005. Prof. Charlene Tsai. Review. Matching Specified by the mean vector of each class Optimum statistical classifiers Probabilistic approach Bayes classifier for Gaussian pattern classes

360 views • 17 slides

Chapter 5: Credibility

Chapter 5: Credibility. Introduction. Performance on the training set is not a good indicator of performance on an independent set. We need to predict performance bounds Quality training data is difficult to obtain---not always in abundance

272 views • 16 slides

Chapter 5: Credibility

Chapter 5: Credibility. Introduction. Performance on the training set is not a good indicator of performance on an independent set. We need to predict performance bounds Quality training data is difficult to obtain---not always in abundance

280 views • 16 slides

Chapter 5: Credibility

Chapter 5: Credibility. Introduction. Performance on the training set is not a good indicator of performance on an independent set. We need to predict performance bounds Quality training data is difficult to obtain---not always in abundance

254 views • 16 slides

Chapter 5: Credibility

Chapter 5: Credibility. Introduction. Performance on the training set is not a good indicator of performance on an independent set. We need to predict performance bounds Quality training data is difficult to obtain---not always in abundance

169 views • 16 slides