'Linear discriminant analysis' presentation slideshows

Image Recoloring

Image Recoloring. Ron Yanovich & Guy Peled. Contents. Grayscale coloring background Luminance / Luminance channel Segmentation Discrete Cosine Transform K-nearest-neighbor ( Knn ) Linear Discriminant Analysis (LDA) Colorization using optimization Colorization by Example (i) Training

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Useful algorithms and statistical methods in Genome data analysis

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Bill Kristan Section of Neurobiology Division of Biological Sciences UC San Diego La Jolla, CA

The dynamics of decision-making by leech neurons Neurobiology of Decision-Making CSH 24 May 2005. Bill Kristan Section of Neurobiology Division of Biological Sciences UC San Diego La Jolla, CA. We make all kinds of choices:. (respond or ignore) .

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Lecture 3. Linear Models for Classification

Lecture 3. Linear Models for Classification. O utline. G e neral framework of Classification Discriminant Analysis Linear discriminant analysis, quadratic discriminant analysis, rank-reduced ~ Logistic Regression Perceptron and Separating Hyperplane. Framework for Classification.

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Support Vector Machines

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Introduction to Machine Learning BMI/IBGP 730

Introduction to Machine Learning BMI/IBGP 730 . Kun Huang Department of Biomedical Informatics The Ohio State University. Machine Learning Statistical learning Artificial intelligence Pattern recognition Data mining. Machine Learning Supervised Unsupervised Semi-supervised

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Classification

Classification. Based in part on Chapter 10 of Hand, Manilla, & Smyth and Chapter 7 of Han and Kamber David Madigan. Predictive Modeling. Goal: learn a mapping: y = f ( x ; ? ) Need: 1. A model structure 2. A score function 3. An optimization strategy

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AI techniques for the game of Go

AI techniques for the game of Go. Erik van der Werf Universiteit Maastricht / ReSound Algorithm R&D. Contents. Introduction Searching techniques The Capture Game Solving Go on Small Boards Learning techniques Move Prediction Learning to Score Predicting Life & Death

596 views • 39 slides

Principal Component Analysis

Principal Component Analysis. Jieping Ye Department of Computer Science and Engineering Arizona State University http://www.public.asu.edu/~jye02. Outline of lecture. What is feature reduction? Why feature reduction? Feature reduction algorithms Principal Component Analysis (PCA)

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The Role of Features, Algorithms and Data in Visual Recognition

The Role of Features, Algorithms and Data in Visual Recognition. Reporter: WuBin Data: 2011.04.29. Outline. Authors Abstract Problem Experiment & Result Discussion Conclusion. Outline. Authors Abstract Problem Experiment & Result Discussion Conclusion. Authors ( Devi Parikh ).

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Regression Analysis

Summer Course: Data Mining. Regression Analysis. Presenter: Georgi Nalbantov. August 2009. Structure. Regression analysis: definition and examples Classical Linear Regression LASSO and Ridge Regression (linear and nonlinear)

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PCA vs ICA vs LDA

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Outline

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Face Recognition

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Linear Discriminant Analysis and Its Variations

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Predicting Depression Occurrence Using Classification Algorithm in Data Mining

Predicting Depression Occurrence Using Classification Algorithm in Data Mining. Abdur Rahman Department of Statistics Shahjalal University of Science and Technology Sylhet , Bangladesh E-mail: airdipu@gmail.com. Introduction. Universal definition of old age is elusive

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Improvements to Statistical Intensity Forecasts

Improvements to Statistical Intensity Forecasts. John A. Knaff, NOAA/NESDIS/STAR, Fort Collins, Colorado, Mark DeMaria, NOAA/NESDIS/STAR, Fort Collins, Colorado, Kate Musgrav e, CIRA/CSU, Fort Collins, Colorado John Kaplan , NOAA/HRD, Miami, Florida

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Linear Discriminant Analysis (LDA)

Linear Discriminant Analysis (LDA). Goal . To classify observations into 2 or more groups based on k discriminant functions (Dependent variable Y is categorical with k classes.) Assumptions Multivariate Normal Distribution variables are distributed normally within the classes/groups.

1.76k views • 10 slides