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
SVM binary classifier ensembles for image classification
Proceedings of the tenth international conference on Information and knowledge management
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Provably Fast Training Algorithms for Support Vector Machines
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
SVM '02 Proceedings of the First International Workshop on Pattern Recognition with Support Vector Machines
Asymptotic behaviors of support vector machines with Gaussian kernel
Neural Computation
SVMTorch: support vector machines for large-scale regression problems
The Journal of Machine Learning Research
Support vector machine active learning with applications to text classification
The Journal of Machine Learning Research
Classifying large data sets using SVMs with hierarchical clusters
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Assessment of the effectiveness of support vector machines for hyperspectral data
Future Generation Computer Systems - Special issue: Geocomputation
Leave-One-Out Bounds for Support Vector Regression Model Selection
Neural Computation
Estimating the Support of a High-Dimensional Distribution
Neural Computation
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Data Selection Using SASH Trees for Support Vector Machines
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
SVMs modeling for highly imbalanced classification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on human computing
Scalable biomedical Named Entity Recognition: investigation of a database-supported SVM approach
International Journal of Bioinformatics Research and Applications
Incrementally maintaining classification using an RDBMS
Proceedings of the VLDB Endowment
Towards a unified architecture for in-RDBMS analytics
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
A general model for continuous noninvasive pulmonary artery pressure estimation
Computers in Biology and Medicine
Can we analyze big data inside a DBMS?
Proceedings of the sixteenth international workshop on Data warehousing and OLAP
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Contemporary commercial databases are placing an increased emphasis on analytic capabilities. Data mining technology has become crucial in enabling the analysis of large volumes of data. Modern data mining techniques have been shown to have high accuracy and good generalization to novel data. However, achieving results of good quality often requires high levels of user expertise. Support Vector Machines (SVM) is a powerful state-of-the-art data mining algorithm that can address problems not amenable to traditional statistical analysis. Nevertheless, its adoption remains limited due to methodological complexities, scalability challenges, and scarcity of production quality SVM implementations. This paper describes Oracle's implementation of SVM where the primary focus lies on ease of use and scalability while maintaining high performance accuracy. SVM is fully integrated within the Oracle database framework and thus can be easily leveraged in a variety of deployment scenarios.