'Input space' presentation slideshows

Prelude

Prelude. A pattern of activation in a NN is a vector A set of connection weights between units is a matrix Vectors and matrices have well-understood mathematical and geometric properties Very useful for understanding the properties of NNs. Operations on Vectors and Matrices. Outline.

1.72k views • 139 slides

Black-Box Testing Techniques I

Black-Box Testing Techniques I. Software Testing and Verification Lecture 4. Prepared by Stephen M. Thebaut, Ph.D. University of Florida. Definition of Black-Box Testing. Testing based solely on analysis of requirements (specification, user documentation, etc.).

1.33k views • 96 slides

648 views • 47 slides

Facility Location in Dynamic Geometric Data Streams

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479 views • 37 slides

Support Vector Machines (SVMs) Chapter 5 (Duda et al.)

Support Vector Machines (SVMs) Chapter 5 (Duda et al.). CS479/679 Pattern Recognition Dr. George Bebis. Learning through empirical risk minimization. Estimate g(x) from a finite set of observations by minimizing an error function, for example, the training error or empirical risk :.

560 views • 36 slides

Software Testing

Software Testing. Input Space Partition Testing. Graphs. Logic. Input Space. Syntax. Applied to. Applied to. Applied to. FSMs. Source. DNF. Specs. Source. Models. Source. Specs. Integ. Input. Design. Use cases. Input Space Coverage. Four Structures for Modeling Software.

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Support Vector Machine (SVM) Classification

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Image Recognition using Hierarchical Temporal Memory

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Aula 4 Radial Basis Function Networks

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Neural Networks

Neural Networks. In Today's Lecture. Pattern Classification Characteristics in Pattern Recognition Perceptron Simple Perceptron for Pattern Classification Perceptron: Algorithm Perceptron: Example Perceptron: Matlab code Assignment 1. Pattern Recognition. What is a Pattern?

437 views • 24 slides

Support Vector Machines (Chapter 3)

Support Vector Machines (Chapter 3). 2004.5.18 ????. Overview. ???????? ??????. Limitation of Linear Learning Machine. L inearly separable ?????????????. ???????????????. Learning in Feature Space. ?(x). x. x. o. ?. ?(x). ?(x). x. o. o. ?(o). ?(x). ?(o). x. o. ?(o). ?(o).

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Fitting Curve Models to Edges

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Kernel Methods for De-noising with Neuroimaging Application

Kernel Methods for De-noising with Neuroimaging Application. Trine Julie Abrahamsen M.Sc. defense, August 10, 2009. Overview. Introduction Kernel Principal Component Analysis The Pre-image Problem Analysis of Cimbi Data Conclusions. Introduction.

362 views • 19 slides

William Gregory Sakas Hunter College, Department of Computer Science

Introduction to Computational Natural Language Learning Linguistics 79400 (Under: Topics in Natural Language Processing ) Computer Science 83000 (Under: Topics in Artificial Intelligence ) The Graduate School of the City University of New York Fall 2001. William Gregory Sakas

350 views • 19 slides

Nonlinear Dimension Reduction:

Nonlinear Dimension Reduction:. Semi-Definite Embedding vs. Local Linear Embedding Li Zhang and Lin Liao. Outline. Nonlinear Dimension Reduction Semi-Definite Embedding Local Linear Embedding Experiments. Dimension Reduction.

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Pattern Recognition and Machine Learning: Kernel Methods

Pattern Recognition and Machine Learning: Kernel Methods. Overview. Many linear parametric models can be recast into an equivalent dual representation in which the predictions are based on linear combinations of a kernel function evaluated at the training data points

419 views • 15 slides

Kernel methods

Navneet Goyal , BITS- Pilani , Rajasthan INDIA. Kernel methods. Figure source: http://wwwold.ini.ruhr-uni-bochum.de/thbio/group/neuralnet/index_p.html. Kernel Methods.

597 views • 15 slides

Software Testing

Software Testing. Testing Levels ( McConnel ). Unit Testing Single programmer involved in writing tested code Component Testing Multiple programmers involved in writing test code Integration Testing Testing two or more independently developed, interacting modules together

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Cascade Correlation

296 views • 10 slides