'Optimization problem' diaporamas de présentation

Shape-Representation

1.25k views • 102 slides

Statistical Learning

Statistical Learning. Dong Liu Dept. EEIS, USTC. Chapter 1. Linear Regression. From one to two Regularization Basis functions Bias-variance decomposition Different regularization forms Bayesian approach. A motivating example 1/2. What is the height of Mount Qomolangma ?

1.52k views • 79 slides

Lagrangian Relaxation and Network Optimization

Lagrangian Relaxation and Network Optimization. Cheng-Ta Lee Department of Information Management National Taiwan University September 29, 2005. Outline. Introduction Problem Relaxations and Branch and Bound Lagrangian Relaxation Technique Lagrangian Relaxation and Linear Programming

1.57k views • 74 slides

Sometimes it Pays to be Greedy: Greedy Algorithms in Economic Epidemiology Fred Roberts, DIMACS

Sometimes it Pays to be Greedy: Greedy Algorithms in Economic Epidemiology Fred Roberts, DIMACS. Optimization Problems in Economic Epidemiology. Many problems in Economic Epi can be formulated as optimization problems: Find a solution that maximizes or minimizes some value.

929 views • 71 slides

The Theory of NP-Completeness

The Theory of NP-Completeness. Review: Finding lower bound by problem transformation. Problem X reduces to problem Y (X ?Y ) iff X can be solved by using any algorithm which solves Y. If X ?Y , Y is more difficult.

864 views • 60 slides

Greedy Algorithms

1.2k views • 57 slides

An Introduction to Latent Dirichlet Allocation (LDA)

An Introduction to Latent Dirichlet Allocation (LDA). David M. Blei, Andrew Y. Ng, and Michael I. Jordan. Latent Dirichlet Allocation. Journal of Machine Learning Research 3 (2003): 993-1022. LDA. A generative probabilistic model for collections of discrete data such text corpora.

3.15k views • 55 slides

New Optimality Conditions and Methods for State-Constrained Elliptic Optimal Control Problems

New Optimality Conditions and Methods for State-Constrained Elliptic Optimal Control Problems Building Bridges between ODE and PDE Optimal Control Michael Frey , Simon Bechmann, Hans Josef Pesch , Armin Rund Chair of Mathematics in Engineering Sciences University of Bayreuth, Germany

771 views • 51 slides

Complex Networks: Models

Complex Networks: Models. Lecture 2. Slides by Panayiotis Tsaparas. What is a network? . Network: a collection of entities that are interconnected with links . people that are friends computers that are interconnected web pages that point to each other proteins that interact.

911 views • 45 slides

15-381 / 681 Instructors: Fei Fang (This Lecture) and Dave Touretzky feifang@cmu

Artificial Intelligence: Representation and Problem Solving Optimization (3): (Mixed) Integer Linear Programming. 15-381 / 681 Instructors: Fei Fang (This Lecture) and Dave Touretzky feifang@cmu.edu Wean Hall 4126. Recap. Convex optimization is a convex function and is a convex set

649 views • 43 slides

Counting position weight matrices in a sequence & an application to discriminative motif finding

Counting position weight matrices in a sequence & an application to discriminative motif finding. Saurabh Sinha Computer Science University of Illinois, Urbana-Champaign. GENE. A C A G TG A. PROTEIN. Transcriptional Regulation. TRANSCRIPTION FACTOR. GENE. A C A G TG A. PROTEIN.

532 views • 43 slides

15-381 / 681 Instructors: Fei Fang (This Lecture) and Dave Touretzky feifang@cmu

Artificial Intelligence: Representation and Problem Solving Optimization (1): Optimization and Convex Optimization. 15-381 / 681 Instructors: Fei Fang (This Lecture) and Dave Touretzky feifang@cmu.edu Wean Hall 4126. Logistics. Complete the CMU 'course hours worked' survey. Recap.

546 views • 36 slides

Pattern Recognition

673 views • 33 slides

Analysis of Algorithms CS 477/677

706 views • 32 slides

Separating hyperplane

Separating hyperplane. Optimal separating hyperplane - support vector classifier. Find the hyperplane that creates the biggest margin between the training points for class 1 and -1. margin. Formulation of the optimization problem. Signed distance to decision border.

600 views • 32 slides

Ultrasonic Beam-forming with the Genetic Algorithm

Ultrasonic Beam-forming with the Genetic Algorithm. Andrew Fiss, Vassar College Nathan Baxter , Ohio Northern University Jerry Magnan , Florida State University. Abstract.

625 views • 31 slides

Optimal Location of Multiple Bleed Points in Rankine Cycle

454 views • 28 slides

Dimensional reduction, PCA

Dimensional reduction, PCA. Curse of dimensionality. The higher the dimension, the more data is needed to draw any conclusion Probability density estimation: Continuous: histograms Discrete: k-factorial designs Decision rules: Nearest-neighbor and K-nearest neighbor.

536 views • 26 slides

Geometry at Work

413 views • 16 slides

Lecture 2(b) Rational Choice and Demand

Lecture 2(b) Rational Choice and Demand. Why It Would Probably Be Ok to Sleep Through This Part of the Lecture. The previous lecture described almost everything you need to know to understand demand. You know what demand functions are about, what demand elasticity means and why it matters.

299 views • 10 slides