Interactive Activation & Competition (Built with Processing)

Interactive Activation & Competition (Built with Processing)

Math Forum - Ask Dr. Math

Math Forum - Ask Dr. Math

inf means "infimum," or "greatest lower bound."  This is  slightly different from minimum in that the greatest lower bound is  defined as:  x is the infimum of the set S [in symbols, x = inf (S)] iff:  a) x is less than or equal to all elements of S b) there is no other number larger than x which is less than or equal    to all elements of S.  Basically, (a) means that x is a lower bound of S, and (b) means that  x is greater than all other lower bounds of S.

pylab_examples example code: line_styles.py — Matplotlib v1.0.1 documentation

pylab_examples example code: line_styles.py

Data mining Blog, Preprocessing, Normalization

Data mining Blog, Preprocessing, Normalization

Outliers can be found by using a technique called box plot (http://en.wikipedia.org/wiki/Box_plot)

Before we normalize the data set, we need to check for outliers

There are two possible ways to handle outliers in the data
1. Ignore them, remove from the data set (or)
2. Reassign the value to of appropriate upper or lower threshold.

Color Proportion & Intensity

Color Proportion & Intensity

pca vs. kpca

A brief intro:

http://www.fml.tuebingen.mpg.de/raetsch/lectures/amsa/7-vonLuxburg-kpca.pdf

"GrabCut"

"GrabCut"

My Linux: Converting PDF to .doc

My Linux: Converting PDF to .doc

KOffice.org » KWord

KOffice.org » KWord

1st International Workshop on Similarity-Based Pattern Analysis and Recognition

1st International Workshop on Similarity-Based Pattern Analysis and Recognition

Foundations and Trends in Machine Learning

Foundations and Trends in Machine Learning

Stat 375

Stat 375 – Inference in Graphical Models

Andrea Montanari, Stanford University, Winter 2011

Graphical models are a unifying framework for describing the statistical relationships between large collections of random variables. Given a graphical model, the most fundamental (and yet highly non-trivial) task is compute the marginal distribution of one or a few such variables. This task is usually referred to as ‘inference’. The focus of this course is on sparse graphical structures, low-complexity inference algorithms, and their analysis. In particular we will treat the following methods: variational inference; message passing algorithms; belief propagation; generalized belief propagation; survey propagation; learning. Applications/examples will include: Gaussian models with sparse inverse covariance; hidden Markov models (Viterbi and BCJR algorithms, Kalman filter); computer vision (segmentation, tracking, etc); constraint satisfaction problems; machine learning (clustering, classification); communications.

Who are some notable machine learning researcher

Machine Learning: Who are some notable machine learning researchers? - Quora

Good Freely Available Textbooks on Machine Learning - MetaOptimize Q+A

Good Freely Available Textbooks on Machine Learning - MetaOptimize Q+A

learn graphic models

What book/articles/course/survey on graphic models would you recommend if a machine learning student wants to dive in this field? - MetaOptimize Q+A

Reinforcement Learning / Successes of Reinforcement Learning

Reinforcement Learning / Successes of Reinforcement Learning

Measuring Measures - Measuring Measures - Learning about Machine Learning, 2nd Ed.

Measuring Measures - Measuring Measures - Learning about Machine Learning, 2nd Ed.

Classifier Showdown « Synaptic

Classifier Showdown « Synaptic

take a closer look at three different classifiers and discuss three different types of classifiers: naive bayesian classifiers, support vector machines and modular multilayer perceptron neural networks.

Ninth Workshop on Mining and Learning with Graphs (MLG 2011)

Ninth Workshop on Mining and Learning with Graphs (MLG 2011)

Stanford School of Engineering - Stanford Engineering Everywhere

Stanford School of Engineering - Stanford Engineering Everywhere