'Stopping criterion' diaporamas de présentation

Data-Intensive Computing with MapReduce

Data-Intensive Computing with MapReduce. Session 5: Graph Processing. Jimmy Lin University of Maryland Thursday, February 21, 2013.

881 views • 69 slides

Clustering IV

Clustering IV. Outline. Impossibility theorem for clustering Density-based clustering and subspace clustering Bi-clustering or co-clustering. General form of impossibility results. Define a set of simple axioms (properties) that a computational task should satisfy

763 views • 48 slides

Using polynomials and matrix splittings to sample from LARGE Gaussians

592 views • 47 slides

OVERVIEW

OVERVIEW. MULTIGRID. Why multigrid? Basic multigrid principles Full multigrid (FMG) Nonlinear multigrid (FAS) Adaptive multigrid. Why Multigrid?. Error Components.

456 views • 31 slides

Data Mining Classification: Alternative Techniques

610 views • 29 slides

Corpora and Statistical Methods Lecture 13

Corpora and Statistical Methods Lecture 13. Albert Gatt. Part 2. Supervised methods. General characterisation. Training set: documents labeled with one or more classes encoded using some representation model Typical data representation model: every document represented as

409 views • 28 slides

From Decision Trees To Rules

From Decision Trees To Rules. Rules are mutually exclusive and exhaustive Rule set contains as much information as the tree. Rules Can Be Simplified. Initial Rule: (Refund=No) ? (Status=Married) ? No Simplified Rule: (Status=Married) ? No. Derive rules from J48.

501 views • 25 slides

Level Sets Framework Refactoring

Level Sets Framework Refactoring. Arnaud Gelas, Kishore Mosaliganti, Nicolas Rannou, Lydie Souhait, Sean Megason Boston 02/03/2011. Outline. Goals Principles Status Traits Fast Marching Image / Mesh Stopping Criterion Constrained Topology Shortest Path Computation

376 views • 25 slides

Level Sets Framework Refactoring

Level Sets Framework Refactoring. Arnaud Gelas, Kishore Mosaliganti, Nicolas Rannou, Lydie Souhait, Sean Megason Boston 02/03/2011. Outline. Goals Principles Status Traits Fast Marching Image / Mesh Stopping Criterion Constrained Topology Shortest Path Computation

251 views • 25 slides

Information Retrieval and Organisation

Information Retrieval and Organisation. Chapter 16 Flat Clustering. Dell Zhang Birkbeck, University of London. What Is Text Clustering?. Text Clustering = G rouping a set of document s into classes of similar documents. Documents within a cluster should be similar.

346 views • 24 slides

Y.-J. Lee & O. L. Mangasarian

Survival-Time Classification of Breast Cancer Patients DIMACS Workshop on Data Mining and Scalable Algorithms August 22-24, 2001- Rutgers University. Y.-J. Lee & O. L. Mangasarian. Data Mining Institute University of Wisconsin - Madison. Second Annual Review June 1, 2001.

298 views • 19 slides

Two Important Parts of SMO (selection heuristics & stopping criterion)

Two Important Parts of SMO (selection heuristics & stopping criterion). A good selection of a pair of updating points will speed up the convergence. The selection heuristics maybe depend on the stopping criterion. Stopping criterion: duality gap

290 views • 17 slides

Decomposition Algorithm

221 views • 11 slides