'Data likelihood' diaporamas de présentation

Clustering as graph cut

Clustering as graph cut. Describe the pairwise distance via a graph. Clustering as graph cut. Describe the pairwise distance via a graph Clustering can be obtained via graph cut. Clustering as graph cut. Describe the pairwise distance via a graph Clustering can be obtained via graph cut.

405 views • 40 slides

Random Forests for Language Modeling

Random Forests for Language Modeling. Peng Xu, Frederick Jelinek. Outline. Basic Language Modeling Language Models Smoothing in n-gram Language Models Decision Tree Language Models Random Forests for Language Models Random Forests n-gram Structured Language Model (SLM) Experiments

554 views • 36 slides

CS 294-5: Statistical Natural Language Processing

CS 294-5: Statistical Natural Language Processing. Text Clustering, EM Lecture 6: 9/19/05 Guest Lecturer: Teg Grenager, Stanford University. Overview. So far: Classification Applications: text categorization, language identification, word sense disambiguation

471 views • 34 slides

CS 294-5: Statistical Natural Language Processing

CS 294-5: Statistical Natural Language Processing. Text Clustering, EM Lecture 6: 9/19/05 Guest Lecturer: Teg Grenager, Stanford University. Overview. So far: Classification Applications: text categorization, language identification, word sense disambiguation

470 views • 34 slides

Language Models

Language Models. Hongning Wang CS@UVa. Stage-1 -Explain unseen words - Dirichlet prior ( Bayesian). Stage-2 -Explain noise in query -2-component mixture. c(w,d). + ?p(w|C). (1- ?). + ?p(w|U). ?. ?. |d|. + ?. Collection LM. P(w|d) =. User background model

411 views • 29 slides

Innovative Front-End Signal Processing

333 views • 21 slides

A particle filter to track multiple objects

222 views • 21 slides