Different Features

Glasses vs. No Glasses

Beard vs. No Beard

Beard Distinction Ghodsi et, al 2007

Glasses Distinction Ghodsi et, al 2007

Multiple-Attribute Metric Ghodsi et, al 2007

Embedding of sparse music similarity graph Platt, 2004

Reinforcement learning Mahadevan and Maggioini, 2005

Semi-supervised learning Use graph-based discretization of manifold to infer missing labels. Build classifiers from bottom eigenvectors of graph Laplacian. Belkin & Niyogi, 2004; Zien et al, Eds., 2005

correspondences

Learning correspondences How can we learn manifold structure that is shared across multiple data sets? c et al, 2003, 2005

Mapping and robot localization Bowling, Ghodsi, Wilkinson 2005 Ham, Lin, D.D. 2005

Classification

Classification

Data

Features (X) (Green, 6, 4, 4.5) (Green, 7, 4.5, 5) (Red, 6, 3, 3.5) (Red, 4.5, 4, 4.5) (Yellow, 1.5, 8, 2) (Yellow, 1.5, 7, 2.5)

Data Representation

Features and labels (Green, 6, 4, 4.5) (Green, 7, 4.5, 5) (Red, 6, 3, 3.5) (Red, 4.5, 4, 4.5) (Yellow, 1.5, 8, 2) (Yellow, 1.5, 7, 2.5) Green Pepper Red Pepper Hot Pepper

Features and labels Objects Features (X)Labels (Y)

Classification (New point) (Red, 7, 4, 4.5) h(Red, 7, 4, 4.5) ?

Classification (New point) (Red, 5, 3, 4.5) h(Red, 5, 3, 4.5) ?

Digit Recognition

Classification

Classification

Classification

Classification

Computer Vision N. Jojic and B.J. Frey, “ Learning flexible sprites in video layers”, CVPR 2001, (Video)Video

Reading Journals: Neural Computation, JMLR, ML, IEEE PAMI Conferences: NIPS, UAI, ICML, AI-STATS, IJCAI, IJCNN Vision: CVPR, ECCV, SIGGRAPH Speech: EuroSpeech, ICSLP, ICASSP Online: citesser, google Books: –Elements of Statistical Learning, Hastie, Tibshirani, Friedman –Learning from Data, Cherkassky, Mulier –Pattern classification, Duda, Hart, Stork –Neural Networks for pattern Recognition, Bishop –Pattern recognition and machine learning, Bishop