'Feature weights' diaporamas de présentation

Maximum Entropy Model (II)

Maximum Entropy Model (II). LING 572 Fei Xia Week 6: 02/08/2011. Outline. Overview The Maximum Entropy Principle Modeling** Decoding Training** Smoothing** Case study: POS tagging: covered in ling570 already. Training. Algorithms.

890 views • 62 slides

Recall Systems: Efficient Learning and Use of Category Indices

Recall Systems: Efficient Learning and Use of Category Indices. Omid Madani With Wiley Greiner, David Kempe, and Mohammad Salavatipour. Overview. Problems and motivation Proposal: recall systems Experiments Related work and conclusions. Massive Learning. Lots of ...

607 views • 51 slides

Spam Detection

Spam Detection. Jingrui He 10/08/2007. Spam Types. Email Spam Unsolicited commercial email Blog Spam Unwanted comments in blogs Splogs Fake blogs to boost PageRank. From Learning Point of View. Spam Detection Classification problem (ham vs. spam) Feature Extraction

2.01k views • 40 slides

CVPR 2009, Miami, Florida

CVPR 2009, Miami, Florida. Object Detection Using a Max-Margin Hough Transform. Subhransu Maji and Jitendra Malik University of California at Berkeley, Berkeley, CA-94720. Overview. Overview of probabilistic Hough transform Learning framework Experiments Summary.

376 views • 24 slides

CVPR 2009, Miami, Florida

CVPR 2009, Miami, Florida. Object Detection Using a Max-Margin Hough Transform. Subhransu Maji and Jitendra Malik University of California at Berkeley, Berkeley, CA-94720. Overview. Overview of probabilistic Hough transform Learning framework Experiments Summary.

245 views • 24 slides

English-Hindi Translation in 21 Days

399 views • 18 slides

Challenges in Mining Social Media Sparsity and Quality

Challenges in Mining Social Media Sparsity and Quality. Dagstuhl Seminar 11171 Challenges in Document Mining Thomas Gottron gottron@uni-koblenz.de. Social Media. Definition from Wikipedia:

286 views • 16 slides

Boosting

Thanks to Citeseer and : A Short Introduction to Boosting. Yoav Freund, Robert E. Schapire, Journal of Japanese Society for Artificial Intelligence,14(5):771-780, September, 1999. Boosting. Feb 18, 2008 10-601 Machine Learning. 1936 - T.

431 views • 13 slides

Building a Maxent Model

Building a Maxent Model. The nuts and bolts. Building a Maxent Model. We define features (indicator functions) over data points Features represent sets of data points which are distinctive enough to deserve model parameters.

229 views • 4 slides