Videos tagged with Machine Learning

A number of important problems in signal processing depend on measures of statistical dependence. For instance, this dependence is minimised in the context of instantaneous ICA, in which linearly mixed signals are separated using their (assumed) pairwise independence from each other. A number of methods have been proposed to measure this dependence, however they generally assume a particular pa...

After a brief introduction to the use of machine learning in computational biology, we focus on the problem of biological networks inference. We define the problem as a problem of kernel learning using prediction in kernelized output spaces. Methods based on Output kernel Tree are presented to solve the problem. Results on two benchmarks are shown. Author: Florence d'Alché, Universit&eac...

Random projection is a simple technique that can often provide insight into questions such as "why is it good to have a large margin?" or "what are kernels really doing and how are they similar to feature selection?" In this talk I will describe some simple learning algorithms using random projection. I will then discuss how, given a kernel as a black-box function, we can us...

Lecture slides: Supervised Learning Outline Outline - Semi-Supervised Learning (SSL) Semi-Supervised Learning (SSL) Outline - Evaluation Evaluation Outiline - Conclusion Conclusion Author: Anastasia Krithara, Xerox Research Centre Europe

Lecture slides: Global (vector) representations Real world challenges Sets of local features Sets of features in vision Sets of features Problem Existing set kernels Partial matching for sets of features Pyramid match kernel Pyramid match overview Feature extraction Counting matches Counting new matches Pyramid match kernel Efficiency Example pyramid match Point sets with 5 to 100 points Learni...

Kernel methods have proven to be very powerful tools in machine learning. In addition, there is a well-developed theory of sufficient conditions for a kernel to be useful for a given learning problem. However, while a kernel function can be thought of as just a pairwise similarity function that satisfies additional mathematical properties, this theory requires viewing kernels as implicit (and o...

Lecture slides: Kernel-based algorithms Regression/Classification Example of regression Regularization RKHS as smoothness penalty Kernel classification/regression Representer theorem Reproducing property Proof of representer theorem Algorithms: RLS RLS demo Algorithms: RLS_ Support Vector Machines Support Vector Machines: Sparsity Feature map interpretation Feature map: RLS Generalization error...

Lecture slides: Linear Regression Example Prediction Problem Linear Model Loss Functions Squared-Error Loss Matrix Notation Minimising MSE Stationary Point Least Squares Solution Prediction Nonlinear Model Probabilistic Regression Noise Distribution Maximum Likelihood Estimate Uncertainty Likelihood Author: Mark Grolami, University of Glasgow

Many problems in computational biology and chemistry can be formalized as classical statistical problems, e.g., pattern recognition, regression or dimension reduction, with the caveat that the data are often not vectors. Indeed objects such as gene sequences, small molecules, protein 3D structures or phylogenetic trees, to name just a few, have particular structures which contain relevant infor...

The lectures will introduce the kernel methods approach to pattern analysis through the particular example of support vector machines for classification. The presentation touches on: generalization, optimization, dual representation, kernel design and algorithmic implementations. We then broaden the discussion to consider general kernel methods by introducing different kernels, different learni...