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Maximum likelihood from incomplete data via the EM algorithm

by A. P. Dempster, N. M. Laird, D. B. Rubin - JOURNAL OF THE ROYAL STATISTICAL SOCIETY, SERIES B , 1977
"... A broadly applicable algorithm for computing maximum likelihood estimates from incomplete data is presented at various levels of generality. Theory showing the monotone behaviour of the likelihood and convergence of the algorithm is derived. Many examples are sketched, including missing value situat ..."
Abstract - Cited by 11972 (17 self) - Add to MetaCart
A broadly applicable algorithm for computing maximum likelihood estimates from incomplete data is presented at various levels of generality. Theory showing the monotone behaviour of the likelihood and convergence of the algorithm is derived. Many examples are sketched, including missing value

Making Large-Scale Support Vector Machine Learning Practical

by Thorsten Joachims , 1998
"... Training a support vector machine (SVM) leads to a quadratic optimization problem with bound constraints and one linear equality constraint. Despite the fact that this type of problem is well understood, there are many issues to be considered in designing an SVM learner. In particular, for large lea ..."
Abstract - Cited by 628 (1 self) - Add to MetaCart
learning tasks with many training examples, off-the-shelf optimization techniques for general quadratic programs quickly become intractable in their memory and time requirements. SVM light1 is an implementation of an SVM learner which addresses the problem of large tasks. This chapter presents

Making Large-Scale SVM Learning Practical

by Thorsten Joachims , 1998
"... Training a support vector machine (SVM) leads to a quadratic optimization problem with bound constraints and one linear equality constraint. Despite the fact that this type of problem is well understood, there are many issues to be considered in designing an SVM learner. In particular, for large lea ..."
Abstract - Cited by 1861 (17 self) - Add to MetaCart
learning tasks with many training examples, off-the-shelf optimization techniques for general quadratic programs quickly become intractable in their memory and time requirements. SV M light1 is an implementation of an SVM learner which addresses the problem of large tasks. This chapter presents algorithmic

Probability: Theory and examples

by Rick Durrett - CAMBRIDGE U PRESS , 2011
"... Some times the lights are shining on me. Other times I can barely see. Lately it occurs to me what a long strange trip its been. Grateful Dead In 1989 when the first edition of the book was completed, my sons David and Greg were 3 and 1, and the cover picture showed the Dow Jones at 2650. The last t ..."
Abstract - Cited by 1331 (16 self) - Add to MetaCart
twenty years have brought many changes but the song remains the same. The title of the book indicates that as we develop the theory, we will focus our attention on examples. Hoping that the book would be a useful reference for people who apply probability in their work, we have tried to emphasize

Learning generative visual models from few training examples: an incremental Bayesian approach tested on 101 object categories

by Li Fei-fei , 2004
"... Abstract — Current computational approaches to learning visual object categories require thousands of training images, are slow, cannot learn in an incremental manner and cannot incorporate prior information into the learning process. In addition, no algorithm presented in the literature has been te ..."
Abstract - Cited by 784 (16 self) - Add to MetaCart
Abstract — Current computational approaches to learning visual object categories require thousands of training images, are slow, cannot learn in an incremental manner and cannot incorporate prior information into the learning process. In addition, no algorithm presented in the literature has been

Text Categorization with Support Vector Machines: Learning with Many Relevant Features

by Thorsten Joachims , 1998
"... This paper explores the use of Support Vector Machines (SVMs) for learning text classifiers from examples. It analyzes the particular properties of learning with text data and identifies, why SVMs are appropriate for this task. Empirical results support the theoretical findings. SVMs achieve substan ..."
Abstract - Cited by 2303 (9 self) - Add to MetaCart
This paper explores the use of Support Vector Machines (SVMs) for learning text classifiers from examples. It analyzes the particular properties of learning with text data and identifies, why SVMs are appropriate for this task. Empirical results support the theoretical findings. SVMs achieve

Training Linear SVMs in Linear Time

by Thorsten Joachims , 2006
"... Linear Support Vector Machines (SVMs) have become one of the most prominent machine learning techniques for high-dimensional sparse data commonly encountered in applications like text classification, word-sense disambiguation, and drug design. These applications involve a large number of examples n ..."
Abstract - Cited by 549 (6 self) - Add to MetaCart
as well as a large number of features N, while each example has only s << N non-zero features. This paper presents a Cutting-Plane Algorithm for training linear SVMs that provably has training time O(sn) for classification problems and O(sn log(n)) for ordinal regression problems. The algorithm

Example-based learning for view-based human face detection

by Kah-kay Sung, Tomaso Poggio - IEEE Transactions on Pattern Analysis and Machine Intelligence , 1998
"... Abstract—We present an example-based learning approach for locating vertical frontal views of human faces in complex scenes. The technique models the distribution of human face patterns by means of a few view-based “face ” and “nonface ” model clusters. At each image location, a difference feature v ..."
Abstract - Cited by 690 (24 self) - Add to MetaCart
Abstract—We present an example-based learning approach for locating vertical frontal views of human faces in complex scenes. The technique models the distribution of human face patterns by means of a few view-based “face ” and “nonface ” model clusters. At each image location, a difference feature

Combining labeled and unlabeled data with co-training

by Avrim Blum, Tom Mitchell , 1998
"... We consider the problem of using a large unlabeled sample to boost performance of a learning algorithm when only a small set of labeled examples is available. In particular, we consider a setting in which the description of each example can be partitioned into two distinct views, motivated by the ta ..."
Abstract - Cited by 1633 (28 self) - Add to MetaCart
data, but our goal is to use both views together to allow inexpensive unlabeled data to augment amuch smaller set of labeled examples. Speci cally, the presence of two distinct views of each example suggests strategies in which two learning algorithms are trained separately on each view, and then each

Object Detection with Discriminatively Trained Part Based Models

by Pedro F. Felzenszwalb, Ross B. Girshick, David McAllester, Deva Ramanan
"... We describe an object detection system based on mixtures of multiscale deformable part models. Our system is able to represent highly variable object classes and achieves state-of-the-art results in the PASCAL object detection challenges. While deformable part models have become quite popular, their ..."
Abstract - Cited by 1422 (49 self) - Add to MetaCart
, their value had not been demonstrated on difficult benchmarks such as the PASCAL datasets. Our system relies on new methods for discriminative training with partially labeled data. We combine a margin-sensitive approach for data-mining hard negative examples with a formalism we call latent SVM. A latent SVM
Results 1 - 10 of 87,030