The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning and making decisions when costs and probabilities are both unknown
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Machine Learning
Accuracy and Stability of Numerical Algorithms
Accuracy and Stability of Numerical Algorithms
Getting Order Independence in Incremental Learning
ECML '93 Proceedings of the European Conference on Machine Learning
A Buffering Strategy to Avoid Ordering Effects in Clustering
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Detecting Concept Drift with Support Vector Machines
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Incremental Learning with Support Vector Machines
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Probabilistic Discriminative Kernel Classifiers for Multi-class Problems
Proceedings of the 23rd DAGM-Symposium on Pattern Recognition
Tree Induction for Probability-Based Ranking
Machine Learning
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
Classes of kernels for machine learning: a statistics perspective
The Journal of Machine Learning Research
Robust Real-Time Face Detection
International Journal of Computer Vision
Efficient Model Selection for Kernel Logistic Regression
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Sparse Multinomial Logistic Regression: Fast Algorithms and Generalization Bounds
IEEE Transactions on Pattern Analysis and Machine Intelligence
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Integrating Representative and Discriminative Models for Object Category Detection
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Predicting good probabilities with supervised learning
ICML '05 Proceedings of the 22nd international conference on Machine learning
A Fast Dual Algorithm for Kernel Logistic Regression
Machine Learning
International Journal of Computer Vision
An empirical comparison of supervised learning algorithms
ICML '06 Proceedings of the 23rd international conference on Machine learning
Principled Hybrids of Generative and Discriminative Models
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Computer Vision and Image Understanding
Solving multiclass support vector machines with LaRank
Proceedings of the 24th international conference on Machine learning
Boosting classifiers for drifting concepts
Intelligent Data Analysis - Knowlegde Discovery from Data Streams
Semi-supervised On-Line Boosting for Robust Tracking
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Multi-conditional learning: generative/discriminative training for clustering and classification
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Constraints on tree structure in concept formation
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 2
Computational Statistics & Data Analysis
Multiple incremental decremental learning of support vector machines
IEEE Transactions on Neural Networks
An online incremental learning support vector machine for large-scale data
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part II
Online Discriminative Kernel Density Estimation
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Bayesian Generalized Kernel Mixed Models
The Journal of Machine Learning Research
Efficient visual object tracking with online nearest neighbor classifier
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part I
IPMI'11 Proceedings of the 22nd international conference on Information processing in medical imaging
Latent Log-Linear Models for Handwritten Digit Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Signal Processing
IEEE Transactions on Audio, Speech, and Language Processing
Incremental linear discriminant analysis for classification of data streams
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Incremental Kernel Principal Component Analysis
IEEE Transactions on Image Processing
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We introduce an innovative incremental learner called incremental import vector machines (I^2VM). The kernel-based discriminative approach is able to deal with complex data distributions. Additionally, the learner is sparse for an efficient training and testing and has a probabilistic output. We particularly investigate the reconstructive component of import vector machines, in order to use it for robust incremental learning. By performing incremental update steps, we are able to add and remove data samples, as well as update the current set of model parameters for incremental learning. By using various standard benchmarks, we demonstrate how I^2VM is competitive or superior to other incremental methods. It is also shown that our approach is capable of managing concept-drifts in the data distributions.