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Geometry at Work

Geometry at Work

Geometry at Work. Adapted by Dr. Sarah from a talk by Dr. Catherine A. Gorini. Computer Learning To Diagnose and Categorize. We will use these tools: • higher-dimensional vector spaces • convex sets • inner products. Geometry in Learning Kristin P. Bennett

By adamdaniel
(243 views)

An Introduction to Latent Dirichlet Allocation (LDA)

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.

By adamdaniel
(2068 views)

Dimensional reduction, PCA

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.

By makya
(219 views)

Complex Networks: Models

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.

By mizell
(397 views)

Shape-Representation

Shape-Representation

Shape-Representation. and. Shape Similarity. Part 1: Shapes. Dr. Rolf Lakaemper. May I introduce myself…. Rolf Lakaemper PhD (Doctorate Degree) 2000 Hamburg University, Germany Currently Assist. Professor at Department of Computer and Information Sciences,

By Lucy
(171 views)

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

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

By sue
(252 views)

Statistical Learning

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 ?

By mercury
(612 views)

Lagrangian Relaxation and Network Optimization

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

By urbana
(404 views)

Analysis of Algorithms CS 477/677

Analysis of Algorithms CS 477/677

Analysis of Algorithms CS 477/677. NP-Completeness Instructor: George Bebis Chapter 34. NP-Completeness. So far we’ve seen a lot of good news!

By lynsey
(272 views)

The Theory of NP-Completeness

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.

By temple
(227 views)

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

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.

By chione
(176 views)

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

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

By cicily
(135 views)

Lecture 2(b) Rational Choice and Demand

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.

By fran
(194 views)

Separating hyperplane

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.

By mckile
(202 views)

Pattern Recognition

Pattern Recognition

Pattern Recognition. Pattern recognition is:. 1. The name of the journal of the Pattern Recognition Society. 2. A research area in which patterns in data are found, recognized, discovered, …whatever. 3. A catchall phrase that includes. classification clustering data mining

By dasha
(253 views)

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

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.

By dane
(207 views)

Optimal Location of Multiple Bleed Points in Rankine Cycle

Optimal Location of Multiple Bleed Points in Rankine Cycle

Optimal Location of Multiple Bleed Points in Rankine Cycle. P M V Subbarao Professor Mechanical Engineering Department I I T Delhi. Sincere Efforts for Best Returns…. A MATHEMATICAL MODEL. A. Turbine. B. SG. Y j-11, h bj-1. y j, h bj. Y j-2, h bj-2. C. OFWH. OFWH. OFWH. C.

By kacy
(149 views)

Ultrasonic Beam-forming with the Genetic Algorithm

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.

By dillon
(221 views)

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

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.

By astra
(91 views)

Greedy Algorithms

Greedy Algorithms

Greedy Algorithms. Greed is good. (Some of the time). Outline. Elements of greedy algorithm Greedy choice property Optimal substructures Minimum spanning tree Kruskal’s algorithm Prim’s algorithm Huffman code Activity selection. Introduction. Concepts

By persephone
(273 views)

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