Support vector machines
Explore this paper's citation graph
Summary
Support vector machines are a family of machine learning methods originally introduced for the problem of classification and later generalized to various other situations, and are currently used in various domains of application, including bioinformatics, text categorization, and computer vision.
- Type
- review
- Published
- 2008-08-12
- Cited by
- 8,589
- References
- 288
- Access
- Open access
- OpenAlex
- https://openalex.org/W2012908927
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:661123
Keywords
Support vector machine, Statistical learning theory, Cluster analysis, Computer science, Categorization
References
- The Nature of Statistical Learning
- A Study on Sigmoid Kernels for SVM and the Training of non-PSD Kernels by SMO-type Methods
- Support Vector Machines for Active Learning in the Drug Discovery Process
- Support vector learning
- The Elements of Statistical Learning: Data Mining, Inference, and Prediction
- Efficient SVM Regression Training with SMO
- Kernels for Semi-Structured Data
- DirectSVM: A Simple Support Vector Machine Perceptron
- On the Relationship Between the Support Vector Machine for Classification and Sparsified Fisher's Linear Discriminant
- Pattern Recognition and Machine Learning
- Kernel Methods for Pattern Analysis
- Feasible Direction Decomposition Algorithms for Training Support Vector Machines
- Text Categorization Based on Regularized Linear Classification Methods
- Theoretical Foundations of the Potential Function Method in Pattern Recognition Learning
- Estimation of Dependences Based on Empirical Data
- Soft Margins for AdaBoost
- Learning to classify text using support vector machines - methods, theory and algorithms
- Extracting Support Data for a Given Task
- Advances in Large Margin Classifiers
- Polynomial-Time Decomposition Algorithms for Support Vector Machines
Cited by
- Goal-driven collaborative filtering
- Fast Learning Rate of lp-MKL and its Minimax Optimality
- Sparsity-Based Generalization Bounds for Predictive Sparse Coding
- Participatory Simulation as a Tool for Agent-based Simulation
- Performance optimization of wind turbines
- Exploiting structure defined by data in machine learning : some new analyses
- Channel compensation for SVM speaker recognition
- Automated Prediction of Preferences Using Facial Expressions
- Single-cell analysis of targeted transcriptome predicts drug sensitivity of single cells within human myeloma tumors
- IRWNRLPI: Integrating Random Walk and Neighborhood Regularized Logistic Matrix Factorization for lncRNA-Protein Interaction Prediction
- Determination of Handedness in a Single Chiral Nanocrystal via Circularly Polarized Luminescence.
- Delta Radiomics Improves Pulmonary Nodule Malignancy Prediction in Lung Cancer Screening
- Continuous Graphical Models for Static and Dynamic Distributions: Application to Structural Biology
- Eyes-free vision-based scanning of aligned barcodes and information extraction from aligned nutrition tables
- Comparative Analysis of AI Techniques to Correct the Inconsistency in the Analytic Hierarchy Process Matrix
- Recovering Distributions from Gaussian RKHS Embeddings
- A comparison of subspace feature-domain methods for language recognition
- PLAL: Cluster-based active learning
- Generalization Bounds for Learning the Kernel Problem
- Attributing Meaning to Online Social Network Analysis for Tailored Socio-Behavioral Support Systems
Related papers
- The Support Vector Machine(SVM) Technique and Its Application
- Radiation Source Threat Assessment based on Support Vector Machine
- Support Vector Machine and Its Applications in NIR Analysis
- Fault Diagnosis Method of Section Track Circuit Based on Support Vector Machine
- Statistical learning theory and state of the art in SVM
- A New Method for Ship Target Recognition Based on Support Vector Machine
- Structure damage severity identification based on support vector machine
- NATURAL GAS LOAD FORECASTING BASED ON LEAST SQUARES SUPPORT VECTOR MACHINE