Learning from hints in neural networks
Journal of Complexity
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Journal of Complexity
Regularization theory and neural networks architectures
Neural Computation
The nature of statistical learning theory
The nature of statistical learning theory
From data distributions to regularization in invariant learning
Neural Computation
SIAM Review
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Solving the quadratic programming problem arising in support vector classification
Advances in kernel methods
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
Combining support vector and mathematical programming methods for classification
Advances in kernel methods
Prior knowledge in support vector kernels
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Statistical and neural classifiers: an integrated approach to design
Statistical and neural classifiers: an integrated approach to design
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Training Invariant Support Vector Machines
Machine Learning
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Transformation Invariance in Pattern Recognition-Tangent Distance and Tangent Propagation
Neural Networks: Tricks of the Trade, this book is an outgrowth of a 1996 NIPS workshop
Incorporating Invariances in Support Vector Learning Machines
ICANN 96 Proceedings of the 1996 International Conference on Artificial Neural Networks
A New Multi-Class SVM Based on a Uniform Convergence Result
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 4 - Volume 4
Recognition and Structure from one 2D Model View: Observations on Prototypes, Object Classes and Symmetries
Best Practices for Convolutional Neural Networks Applied to Visual Document Analysis
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
On the algorithmic implementation of multiclass kernel-based vector machines
The Journal of Machine Learning Research
Learning over sets using kernel principal angles
The Journal of Machine Learning Research
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Incorporating prior knowledge with weighted margin support vector machines
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Incorporating Prior Knowledge into SVM for Image Retrieval
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Tangent Vector Kernels for Invariant Image Classification with SVMs
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Fast SVM Training Algorithm with Decomposition on Very Large Data Sets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Core Vector Machines: Fast SVM Training on Very Large Data Sets
The Journal of Machine Learning Research
Analysis of errors of handwritten digits made by a multitude of classifiers
Pattern Recognition Letters - Special issue: In memoriam Azriel Rosenfeld
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Improvements to Platt's SMO Algorithm for SVM Classifier Design
Neural Computation
ICML '06 Proceedings of the 23rd international conference on Machine learning
Invariances in kernel methods: From samples to objects
Pattern Recognition Letters
Working Set Selection Using Second Order Information for Training Support Vector Machines
The Journal of Machine Learning Research
A trainable feature extractor for handwritten digit recognition
Pattern Recognition
A fast parallel optimization for training support vector machine
MLDM'03 Proceedings of the 3rd international conference on Machine learning and data mining in pattern recognition
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Joint manifold distance: a new approach to appearance based clustering
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Invariance in kernel methods by haar-integration kernels
SCIA'05 Proceedings of the 14th Scandinavian conference on Image Analysis
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Incorporating prior knowledge in support vector regression
Machine Learning
Support vector regression from simulation data and few experimental samples
Information Sciences: an International Journal
Switched and PieceWise Nonlinear Hybrid System Identification
HSCC '08 Proceedings of the 11th international workshop on Hybrid Systems: Computation and Control
Multi-view kernel machine on single-view data
Neurocomputing
Guest editorial: special issue on structured prediction
Machine Learning
A Novel Regularization Learning for Single-View Patterns: Multi-View Discriminative Regularization
Neural Processing Letters
Online knowledge-based support vector machines
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part II
Using local alignments for relation recognition
Journal of Artificial Intelligence Research
IEEE Transactions on Information Technology in Biomedicine - Special section on affective and pervasive computing for healthcare
Face recognition method based on support vector machine and particle swarm optimization
Expert Systems with Applications: An International Journal
Support Vector Machine incorporated with feature discrimination
Expert Systems with Applications: An International Journal
Incorporating a priori knowledge from detractor points into support vector classification
ICANNGA'11 Proceedings of the 10th international conference on Adaptive and natural computing algorithms - Volume Part II
Regression based on support vector classification
ICANNGA'11 Proceedings of the 10th international conference on Adaptive and natural computing algorithms - Volume Part II
Proceedings of the 2nd ACM Conference on Bioinformatics, Computational Biology and Biomedicine
Multivariate convex support vector regression with semidefinite programming
Knowledge-Based Systems
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
Exploring weakly supervised latent sentiment explanations for aspect-level review analysis
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Self-advising support vector machine
Knowledge-Based Systems
A theoretical framework for supervised learning from regions
Neurocomputing
Modeling financial dependence with support vector regression
Intelligent Data Analysis
Automated detecting and classifying of sleep apnea syndrome based on genetic-SVM
International Journal of Hybrid Intelligent Systems
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For classification, support vector machines (SVMs) have recently been introduced and quickly became the state of the art. Now, the incorporation of prior knowledge into SVMs is the key element that allows to increase the performance in many applications. This paper gives a review of the current state of research regarding the incorporation of two general types of prior knowledge into SVMs for classification. The particular forms of prior knowledge considered here are presented in two main groups: class-invariance and knowledge on the data. The first one includes invariances to transformations, to permutations and in domains of input space, whereas the second one contains knowledge on unlabeled data, the imbalance of the training set or the quality of the data. The methods are then described and classified into the three categories that have been used in literature: sample methods based on the modification of the training data, kernel methods based on the modification of the kernel and optimization methods based on the modification of the problem formulation. A recent method, developed for support vector regression, considers prior knowledge on arbitrary regions of the input space. It is exposed here when applied to the classification case. A discussion is then conducted to regroup sample and optimization methods under a regularization framework.