'Local minima' presentation slideshows

Graph P artitioning a nd Clustering for Community Detection

1.79k views • 153 slides

Group 1

1.05k views • 87 slides

Centerline detection of (cardiac) vessels in CT images Martin Korevaar

Centerline detection of (cardiac) vessels in CT images Martin Korevaar. Supervisors: Shengjun Wang Han van Triest Yan Kang Bart ter Haar Romenij. Overview. Introduction Method Feature space Minimal Cost Path (MCP) search Results Conclusion Discussion. Introduction.

614 views • 49 slides

Ayema Aduku Oluwaseun Harris Valerie Rivera Miguel Bagajewicz

Ayema Aduku Oluwaseun Harris Valerie Rivera Miguel Bagajewicz. Evaluation of LNG Production Technologies. University of Oklahoma. Outline. LNG Background Objective Simulation Specifications Liquefaction Techniques Heat Exchanger Types Simulation Method Results.

940 views • 43 slides

544 views • 36 slides

Genetic Algorithm in Job Shop Scheduling

357 views • 34 slides

The Perceptron

The Perceptron. Neural Networks 3. Truth Table for Logical AND. x. y. x & y. 0. 0 . 0. 1 . 1 . 0. 1 . 1 . output. inputs. Prehistory.

581 views • 34 slides

534 views • 34 slides

Chapter 1 Introduction

430 views • 33 slides

Learn from chips: Microarray data analysis and clustering CS 374 Yu Bai Nov. 16, 2004

Learn from chips: Microarray data analysis and clustering CS 374 Yu Bai Nov. 16, 2004. Outlines. Background & motivation Algorithms overview fuzzy k-mean clustering (1 st paper) Independent component analysis(2 nd paper). CHIP-ing away at medical questions . Why does cancer occur?.

429 views • 31 slides

Machine Learning

Machine Learning. Lecture 9 Support Vector Machines. Based on slides from Prof. Ray Mooney . Perceptron Revisited: Linear Separators . Binary classification can be viewed as the task of separating classes in feature space:. w T x + b = 0. w T x + b > 0. w T x + b < 0.

674 views • 24 slides

Chapter 9 Heuristics in Planning

Lecture slides for Automated Planning: Theory and Practice. Chapter 9 Heuristics in Planning. Dana S. Nau University of Maryland 1:28 PM July 16, 2014. Planning as Nondeterministic Search. Making it Deterministic. Digression: the A* algorithm (on trees).

323 views • 23 slides

3.2 Graphs of Functions

3.2 Graphs of Functions. Objectives: Determine whether a graph represents a function. Analyze graphs to determine domain and range, local maxima and minima, inflection points, and intervals where they are increasing, decreasing, concave up, and concave down. Graph parametric equations.

394 views • 23 slides

Unsupervised Learning and Clustering

Unsupervised Learning and Clustering. In unsupervised learning you are given a data set with no output classifications Clustering is an important type of unsupervised learning Association learning was another type of unsupervised learning

441 views • 20 slides

RPROP Resilient Propagation

RPROP Resilient Propagation. Students: Novica Zarvic Roxana Grigoras Term: Winter semester 2003/2004 Lecture: Machine Learning / Neural Networks Course: Information Engineering Date: 2004-01-06. Content. Part I: General remarks

1.69k views • 19 slides

Math Refresher for STAT 391

Math Refresher for STAT 391. Bhushan Mandhani, TA. Topics Covered. Differential Calculus Integral Calculus Univariate optimization. Elementary Functions. Polynomials Exponential and Logarithmic functions. Trigonometric and Inverse Trigonometric.

403 views • 18 slides

Lecture 9&10: Multi-Layer Perceptrons

Lecture 9&10: Multi-Layer Perceptrons. x 0 =1. h 0 =1. y. h 1. x 1. x 2. h 2. Dr Martin Brown Room: E1k Email: martin.brown@manchester.ac.uk Telephone: 0161 306 4672 http://www.eee.manchester.ac.uk/intranet/pg/coursematerial/. Lecture 9&10: Outline.

330 views • 16 slides

K-Means

K-Means. Michael Jones ENEE698Q Fall 2011. Overview. Introduction Problem Formulation How K-Means Works Pros and Cons of Using K-Means How to Improve K-Means K-Means on a Manifold Vector Quantization. Introduction.

597 views • 14 slides

Image Segmentation Using Physical Models

260 views • 12 slides

Exact and Approximate Inference in Associative Hierarchical Networks using Graph Cuts

221 views • 1 slides