Results 1  10
of
2,898,466
The Nature of Statistical Learning Theory
, 1999
"... Statistical learning theory was introduced in the late 1960’s. Until the 1990’s it was a purely theoretical analysis of the problem of function estimation from a given collection of data. In the middle of the 1990’s new types of learning algorithms (called support vector machines) based on the deve ..."
Abstract

Cited by 12976 (32 self)
 Add to MetaCart
Statistical learning theory was introduced in the late 1960’s. Until the 1990’s it was a purely theoretical analysis of the problem of function estimation from a given collection of data. In the middle of the 1990’s new types of learning algorithms (called support vector machines) based
The tradeoffs of large scale learning
 IN: ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 20
, 2008
"... This contribution develops a theoretical framework that takes into account the effect of approximate optimization on learning algorithms. The analysis shows distinct tradeoffs for the case of smallscale and largescale learning problems. Smallscale learning problems are subject to the usual approx ..."
Abstract

Cited by 257 (4 self)
 Add to MetaCart
This contribution develops a theoretical framework that takes into account the effect of approximate optimization on learning algorithms. The analysis shows distinct tradeoffs for the case of smallscale and largescale learning problems. Smallscale learning problems are subject to the usual
Making LargeScale SVM Learning Practical
, 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 1846 (17 self)
 Add to MetaCart
learning tasks with many training examples, offtheshelf 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
Making LargeScale Support Vector Machine Learning Practical
, 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 620 (1 self)
 Add to MetaCart
learning tasks with many training examples, offtheshelf 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
CYC: A LargeScale Investment in Knowledge Infrastructure
 Communications of the ACM
, 1995
"... This article examines the fundamental ..."
Largescale machine learning with stochastic gradient descent
 in COMPSTAT
, 2010
"... Abstract. During the last decade, the data sizes have grown faster than the speed of processors. In this context, the capabilities of statistical machine learning methods is limited by the computing time rather than the sample size. A more precise analysis uncovers qualitatively different tradeoffs ..."
Abstract

Cited by 148 (1 self)
 Add to MetaCart
for the case of smallscale and largescale learning problems. The largescale case involves the computational complexity of the underlying optimization algorithm in nontrivial ways. Unlikely optimization algorithms such as stochastic gradient descent show amazing performance for largescale problems
SNOPT: An SQP Algorithm For LargeScale Constrained Optimization
, 2002
"... Sequential quadratic programming (SQP) methods have proved highly effective for solving constrained optimization problems with smooth nonlinear functions in the objective and constraints. Here we consider problems with general inequality constraints (linear and nonlinear). We assume that first deriv ..."
Abstract

Cited by 582 (23 self)
 Add to MetaCart
Sequential quadratic programming (SQP) methods have proved highly effective for solving constrained optimization problems with smooth nonlinear functions in the objective and constraints. Here we consider problems with general inequality constraints (linear and nonlinear). We assume that first
Imagenet: A largescale hierarchical image database
 In CVPR
, 2009
"... The explosion of image data on the Internet has the potential to foster more sophisticated and robust models and algorithms to index, retrieve, organize and interact with images and multimedia data. But exactly how such data can be harnessed and organized remains a critical problem. We introduce her ..."
Abstract

Cited by 808 (29 self)
 Add to MetaCart
datasets. Constructing such a largescale database is a challenging task. We describe the data collection scheme with Amazon Mechanical Turk. Lastly, we illustrate the usefulness of ImageNet through three simple applications in object recognition, image classification and automatic object clustering. We
The Anatomy of a LargeScale Hypertextual Web Search Engine
 COMPUTER NETWORKS AND ISDN SYSTEMS
, 1998
"... In this paper, we present Google, a prototype of a largescale search engine which makes heavy use of the structure present in hypertext. Google is designed to crawl and index the Web efficiently and produce much more satisfying search results than existing systems. The prototype with a full text an ..."
Abstract

Cited by 4609 (5 self)
 Add to MetaCart
depth description of our largescale web search engine  the first such detailed public description we know of to date.
Apart from the problems of scaling traditional search techniques to data of this magnitude, there are new technical challenges involved with using the additional information present in hypertext
Pregel: A system for largescale graph processing
 IN SIGMOD
, 2010
"... Many practical computing problems concern large graphs. Standard examples include the Web graph and various social networks. The scale of these graphs—in some cases billions of vertices, trillions of edges—poses challenges to their efficient processing. In this paper we present a computational model ..."
Abstract

Cited by 472 (0 self)
 Add to MetaCart
Many practical computing problems concern large graphs. Standard examples include the Web graph and various social networks. The scale of these graphs—in some cases billions of vertices, trillions of edges—poses challenges to their efficient processing. In this paper we present a computational
Results 1  10
of
2,898,466