The nature of statistical learning theory
The nature of statistical learning theory
Making large-scale support vector machine learning practical
Advances in kernel methods
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Using analytic QP and sparseness to speed training of support vector machines
Proceedings of the 1998 conference on Advances in neural information processing systems II
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
The Kernel-Adatron Algorithm: A Fast and Simple Learning Procedure for Support Vector Machines
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Predicting Time Series with Support Vector Machines
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
Successive overrelaxation for support vector machines
IEEE Transactions on Neural Networks
Improvements to the SMO algorithm for SVM regression
IEEE Transactions on Neural Networks
Extracting query modifications from nonlinear SVMs
Proceedings of the 11th international conference on World Wide Web
A note on the decomposition methods for support vector regression
Neural Computation
Training v-support vector regression: theory and algorithms
Neural Computation
A New Cache Replacement Algorithm in SMO
SVM '02 Proceedings of the First International Workshop on Pattern Recognition with Support Vector Machines
SVM '02 Proceedings of the First International Workshop on Pattern Recognition with Support Vector Machines
Fast SVM Training Algorithm with Decomposition on Very Large Data Sets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Entity resolution in geospatial data integration
GIS '06 Proceedings of the 14th annual ACM international symposium on Advances in geographic information systems
An improved way tomake large-scale SVR learning practical
EURASIP Journal on Applied Signal Processing
A POD-Based Center Selection for RBF Neural Network in Time Series Prediction Problems
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part II
Global Convergence Analysis of Decomposition Methods for Support Vector Regression
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks
Expert Systems with Applications: An International Journal
Arbitrary norm support vector machines
Neural Computation
Candidate working set strategy based SMO algorithm in support vector machine
Information Processing and Management: an International Journal
Study of the SMO algorithm applied in power system load forecasting
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Evolutionary feature and parameter selection in support vector regression
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
Fuzzy integral to speed up support vector machines training for pattern classification
International Journal of Knowledge-based and Intelligent Engineering Systems
Combining logic and probabilities for discovering mappings between taxonomies
KSEM'10 Proceedings of the 4th international conference on Knowledge science, engineering and management
A shift-invariant morphological system for software development cost estimation
Expert Systems with Applications: An International Journal
Discovery of probabilistic mappings between taxonomies: principles and experiments
Journal on data semantics XV
Using support vector machine for modeling of pulsed GTAW process
IDEAL'05 Proceedings of the 6th international conference on Intelligent Data Engineering and Automated Learning
Concept updating with support vector machines
WAIM'05 Proceedings of the 6th international conference on Advances in Web-Age Information Management
Kernel-Based reinforcement learning
ICIC'06 Proceedings of the 2006 international conference on Intelligent Computing - Volume Part I
Short term load forecasting model based on support vector machine
ICMLC'05 Proceedings of the 4th international conference on Advances in Machine Learning and Cybernetics
Mutual conversion of regression and classification based on least squares support vector machines
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Support vector machine regression algorithm based on chunking incremental learning
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part I
A simple quantile regression via support vector machine
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
A modified SMO algorithm for SVM regression and its application in quality prediction of HP-LDPE
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
A novel sequential minimal optimization algorithm for support vector regression
ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
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The sequential minimal optimization algorithm (SMO) has been shown to be an effective method for training support vector machines (SVMs) on classification tasks defined on sparse data sets. SMO differs from most SVM algorithms in that it does not require a quadratic programming solver. In this work, we generalize SMO so that it can handle regression problems. However, one problem with SMO is that its rate of convergence slows down dramatically when data is non-sparse and when there are many support vectors in the solution—as is often the case in regression—because kernel function evaluations tend to dominate the runtime in this case. Moreover, caching kernel function outputs can easily degrade SMO's performance even more because SMO tends to access kernel function outputs in an unstructured manner. We address these problems with several modifications that enable caching to be effectively used with SMO. For regression problems, our modifications improve convergence time by over an order of magnitude.