Assignment 5 Midterm LARGE MARGIN CLASSIFIERS.
To tackle this problem, the Large-margin Unified Machine (LUM) was recently proposed as a unified family to embrace both groups. The LUM family enables one to study the behavior change from soft to hard binary classifiers. For multicategory cases, however, the concept of soft and hard classification becomes less clear.
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): Abstract. We consider the problem of binary classification where the classifier can, for a particular cost, choose not to classify an observation. Just as in the conventional classification problem, minimization of the sample average of the cost is a difficult optimization problem.
Secondly, most classification systems assume that the data used to train and test the classifier are drawn from an independent and identically distributed (i.i.d.) underlying distribution. Nevertheless, this assumption is commonly violated in many real-life problems where sub-groups of samples have a high degree of correlation amongst both their features and their labels.
Classification and Regression. Lower bounds on the performance of polynomial-time algorithms for sparse linear regression. Y. Zhang, M. Wainwright, and M. I. Jordan. Proceedings of the Conference on Computational Learning Theory (COLT), Barcelona, Spain, 2014. EP-GIG priors and applications in Bayesian sparse learning. Z.
Supervised Classification and Unsupervised Classification. Once trained, the classifier is then used to attach labels to all the image pixels according to the trained parameters. The most commonly used supervised. dark water, large lumps of bright-white thick clouds (high clouds) with light yellow clouds around them, small lumps of.
Maximizing the margin seems good because points near the decision (interval) represent very uncertain classification decisions: there is almost a 50% chance of the classifier deciding either way. A classifier with a large margin makes no low certainty classification decisions.
Pattern classification and large margin classifiers (Slides: ps) To be presented at the 2006 Machine Learning Summer School, National Taiwan University of Science and Technology, Taipei, Taiwan., August 2-3, 2006. Empirical Minimization and Risk Bounds (Slides: ps) Statistical Properties of Large Margin Classifiers (Slides: ps, pdf).