'Minimum description length' diaporamas de présentation

Cluster validation

1.71k views • 94 slides

Towards a new empiricism in linguistics

Towards a new empiricism in linguistics. John A. Goldsmith The University of Chicago. A touch of history. Rationalists. Empiricists. Logical positivism, logical empiricism. A new rationalism. 20 th Century. Noam Chomsky. Hans Reichenbach. Rudolf Carnap. Finding a synthesis .

1.1k views • 76 slides

Statistical approaches to language learning

844 views • 66 slides

A Quick Romp Through Probabilistic Relational Models

A Quick Romp Through Probabilistic Relational Models. Teg Grenager NLP Lunch February 13, 2003. Agenda. Bayesian Networks Probabilistic Relational Models Learning PRMs Extensions and Alternative Models Applications to NLP. Agenda. Bayesian Networks Probabilistic Relational Models

656 views • 50 slides

On the Need to Bootstrap Ontology Learning with Extraction Grammar Learning

605 views • 42 slides

From Dirt to Shovels: Inferring PADS descriptions from ASCII Data

From Dirt to Shovels: Inferring PADS descriptions from ASCII Data. Kathleen Fisher David Walker Peter White Kenny Zhu. July 2007. Government stats. "MSN","YYYYMM","Publication Value","Publication Unit","Column Order" "TEAJBUS",197313,-0.456483,Quadrillion Btu,4

550 views • 41 slides

Model Selection and Validation

514 views • 39 slides

CMSC 671 Fall 2010

413 views • 32 slides

Nonrigid Shape Correspondence using Landmark Sliding, Insertion, and Deletion

Nonrigid Shape Correspondence using Landmark Sliding, Insertion, and Deletion. Theodor Richardson. Overview. Statistical Shape Analysis (SSA) is growing in usage (mainly to develop models for better image segmentation) Accurate SSA methods depend upon an accurate shape correspondence.

463 views • 27 slides

Bayesian Learning

Bayesian Learning. Rong Jin. Outline. MAP learning vs. ML learning Minimum description length principle Bayes optimal classifier Bagging. Maximum Likelihood Learning (ML). Find the model that best model by maximizing the log-likelihood of the training data Logistic regression

468 views • 27 slides

Bayesian Learning

Bayesian Learning. Rong Jin. Outline. MAP learning vs. ML learning Minimum description length principle Bayes optimal classifier Bagging. Maximum Likelihood Learning (ML). Find the model that best model by maximizing the log-likelihood of the training data Logistic regression

284 views • 27 slides

SLIQ: A Fast Scalable Classifier for Data Mining

SLIQ: A Fast Scalable Classifier for Data Mining. Manish Mehta, Rakesh Agrawal, Jorma Rissanen 1996. Presentation by: Vladan Radosavljevic. Outline. Introduction Motivation SLIQ Algorithm Building tree Pruning Example Results Conclusion. Introduction.

497 views • 24 slides

Wednesday, 28 February 2007 William H. Hsu Department of Computing and Information Sciences, KSU

Lecture 19 of 42. MAP and MLE continued, Minimum Description Length (MDL). Wednesday, 28 February 2007 William H. Hsu Department of Computing and Information Sciences, KSU http://www.kddresearch.org Readings for next class: Chapter 5, Mitchell. Lecture Outline.

330 views • 24 slides

Applying the SUBDUE Substructure Discovery System to the Chemical Toxicity Domain

Applying the SUBDUE Substructure Discovery System to the Chemical Toxicity Domain. Ravindra N. Chittimoori, Diane J. Cook, Lawrence B. Holder Department of Computer Science and Engineering University of Texas at Arlington http://cygnus.uta.edu/subdue/. Motivation and Goal.

246 views • 16 slides

mage registration by model criteria

mage registration by model criteria. R. S. Schestowitz, C. J. Twining, T. F. Cootes and C. J. Taylor. Overview. Non-rigid registration (NRR) Registration and models Experiments Models as a similarity measure Toward automatic appearance model construction Results Conclusions.

250 views • 12 slides