Videos tagged with Linear Models

Multiplicative update rules have proven useful in many areas of machine learning. Simple to implement, guaranteed to converge, they account in part for the widespread popularity of algorithms such as nonnegative matrix factorization and Expectation-Maximization. In this paper, we show how to derive multiplicative updates for problems in L1-regularized linear and logistic regression. For L1&ndas...

In this introductory course we will discuss how log linear models can be extended to feature space. These log linear models have been studied by statisticians for a long time under the name of exponential family of probability distributions. We provide a unified framework which can be used to view many existing kernel algorithms as special cases. Our framework also allows us to derive many natu...

In this introductory course we will discuss how log linear models can be extended to feature space. These log linear models have been studied by statisticians for a long time under the name of exponential family of probability distributions. We provide a unified framework which can be used to view many existing kernel algorithms as special cases. Our framework also allows us to derive many natu...

In this introductory course we will discuss how log linear models can be extended to feature space. These log linear models have been studied by statisticians for a long time under the name of exponential family of probability distributions. We provide a unified framework which can be used to view many existing kernel algorithms as special cases. Our framework also allows us to derive many natu...

In this course I will discuss how exponential families, a standard tool in statistics, can be used with great success in machine learning to unify many existing algorithms and to invent novel ones quite effortlessly. In particular, I will show how they can be used in feature space to recover Gaussian Process classification for multiclass discrimination, sequence annotation (via Conditional Rand...

Over the last five years, we have been able to extend the theory of linear classifiers to structure prediction problems, combining the benefits of discriminative learning and of structured probabilistic models like hidden Markov models. I will review these models and their learning algorithms, and exemplify their use in text processing, with a focus on information extraction from biomedical tex...

In this introductory course we will discuss how log linear models can be extended to feature space. These log linear models have been studied by statisticians for a long time under the name of exponential family of probability distributions. We provide a unified framework which can be used to view many existing kernel algorithms as special cases. Our framework also allows us to derive many natu...

The l-bfgs limited-memory quasi-Newton method is the algorithm of choice for optimizing the parameters of large-scale log-linear models with L2 regularization, but it cannot be used for an L1-regularized loss due to its non-differentiability whenever some parameter is zero. Eficient algorithms have been proposed for this task, but they are impractical when the number of parameters is very large...

In this introductory course we will discuss how log linear models can be extended to feature space. These log linear models have been studied by statisticians for a long time under the name of exponential family of probability distributions. We provide a unified framework which can be used to view many existing kernel algorithms as special cases. Our framework also allows us to derive many natu...