Thorsten Joachims  Home/Search
Context Related
| ai.cs.unidortmund...Joachims_98c.ps.gz statistik.unidortmund.de/...tr2898.ps Cached: PS.gz PS PDF This document uses CoBlitz to cache paper downloads. If your firewall is blocking outgoing connections to port 3125, you can use these links to download local copies.
Image Update HelpPS.gz PS PDF From: ai.informatik.unid...reports.eng (more) Homepages: T.Joachims |
Rate this article:      (best)Comment on this article |
Abstract: 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 learning tasks with many training examples, off-the-shelf optimization techniques for general quadratic programs quickly become intractable in their memory and time requirements. SVMlight is an... (Update)
Cited by: More
Loss Functions and Structured Domains for Support Vector Machines - Portera (2005) (Correct)
Machine Vision and Applications manuscript No. - Will Be Inserted (2003) (Correct)
Multiple Kernel Learning, Conic Duality, and the SMO Algorithm - Francis Bach Gert (Correct)
Similar documents (at the sentence level):
29.5%: The Maximum-Margin Approach to Learning Text Classifiers -.. - Joachims (2000) (Correct)
Active bibliography (related documents): More All
0.4: Unknown - At Dortmund Lehrstuhl (Correct)
0.4: /08/25 16:31 - Preface Statistical (Correct)
0.4: Advances in Kernel Methods - Support Vector Learning - Schölkopf, Burges, (Eds.) (1998) (Correct)
Similar documents based on text: More All
0.3: Estimating the Generalization Performance of an SVM Efficiently - Joachims (1999) (Correct)
0.2: Text Categorization with Support Vector Machines: Learning with.. - Joachims (1998) (Correct)
0.2: Classification Based on the Support Vector Machine.. - Christmann, Fischer.. (2002) (Correct)
Related documents from co-citation: More All
23: The Nature of Statistical Learning Theory (context) - Vapnik - 1995
18: Fast training of support vector machines using sequential minimal optimization (context) - Platt - 1998
17: A tutorial on support vector machines for pattern recognition - Burges - 1998
BibTeX entry: (Update)
T. Joachims, Making large-scale support vector machine learning practical, in B. Scholkopf, C. Burges, A. Smola. Advances in Kernel Methods: Support Vector Machines, MIT Press, Cambridge, MA, December 1998. http://citeseer.ist.psu.edu/joachims98making.html More
@incollection{ joachims98making,
author = "T. Joachims",
title = "Making large-scale support vector machine learning practical",
booktitle = "Advances in Kernel Methods: Support
Vector Machines"
editor = "B. Sch{\"o}lkopf, C. Burges, A. Smola",
publisher = "MIT Press, Cambridge, MA",
year = "1998",
url = "citeseer.ist.psu.edu/joachims98making.html",
url = "citeseer.nj.nec.com/joachims98making.html" }
Citations (may not include all citations):1291 The Nature of Statistical Learning Theory (context) - Vapnik - 1995
376 Text categorization with support vector machines - Joachims - 1998
80 Support vector machines: Training and applications - Osuna, Freund et al. - 1996
25 Solving the quadratic programming problem arising in support.. (context) - Kaufman - 1998
5 Optimization - Theory and Applications (context) - Werner - 1984
5 Methods of Feasible Directions: a Study in Linear and Non-li.. (context) - Zoutendijk - 1970
The graph only includes citing articles where the year of publication is known.
Documents on the same site (http://www-ai.informatik.uni-dortmund.de/FORSCHUNG/REPORTS/reports.eng.html): More
Inferring Probabilistic Automata from Sensor Data for Robot.. - Rieger (1995) (Correct)
Turning an Action Formalism into a Planner - A Case Study - Hertzberg, Thiébaux (1994) (Correct)
A three-valued logic for Inductive Logic Programming - Bell, Weber (1993) (Correct)
Online articles have much greater impact More about CiteSeer.IST Add search form to your site Submit documents Feedback
CiteSeer.IST - Copyright Penn State and NEC