The nature of statistical learning theory
The nature of statistical learning theory
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Handling concept drifts in incremental learning with support vector machines
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Incremental Support Vector Machine Construction
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Incremental Support Vector Learning: Analysis, Implementation and Applications
The Journal of Machine Learning Research
Efficient instance-based learning on data streams
Intelligent Data Analysis
A multilayered neuro-fuzzy classifier with self-organizing properties
Fuzzy Sets and Systems
Performance enhancement for neural fuzzy systems using asymmetric membership functions
Fuzzy Sets and Systems
Support vector machine techniques for nonlinear equalization
IEEE Transactions on Signal Processing
Comparing support vector machines with Gaussian kernels to radialbasis function classifiers
IEEE Transactions on Signal Processing
Musical instrument recognition by pairwise classification strategies
IEEE Transactions on Audio, Speech, and Language Processing
An approach to online identification of Takagi-Sugeno fuzzy models
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Human Body Posture Classification by a Neural Fuzzy Network and Home Care System Application
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
An online self-constructing neural fuzzy inference network and its applications
IEEE Transactions on Fuzzy Systems
Support vector learning for fuzzy rule-based classification systems
IEEE Transactions on Fuzzy Systems
Support vector learning mechanism for fuzzy rule-based modeling: a new approach
IEEE Transactions on Fuzzy Systems
Support-vector-based fuzzy neural network for pattern classification
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Support vector machines for histogram-based image classification
IEEE Transactions on Neural Networks
Fuzzy basis functions, universal approximation, and orthogonal least-squares learning
IEEE Transactions on Neural Networks
Hierarchical Singleton-Type Recurrent Neural Fuzzy Networks for Noisy Speech Recognition
IEEE Transactions on Neural Networks
Navigating interpretability issues in evolving fuzzy systems
SUM'12 Proceedings of the 6th international conference on Scalable Uncertainty Management
Robust support vector machine-trained fuzzy system
Neural Networks
| Hi-index | 0.21 |
This paper proposes an incremental support vector machine-trained TS-type fuzzy classifier (ISVM-FC). The ISVM-FC is a fuzzy system that consists of Takagi-Sugeno (TS)-type fuzzy rules. Structure and parameters in the ISVM-FC are trained incrementally from one subset of training data at a time. This incremental training approach avoids the use of large amounts of memory required for storing training data in batch learning, reduces training time, and adapts the classifier to time-dependent classification systems where training data are available sequentially. Initially, there are no fuzzy rules for structure learning with the ISVM-FC. It generates all rules according to the distribution of the training data. An incremental linear support vector machine (SVM) is used to tune the resulting rule parameters to give the classifier better generalization performance. The use of incremental learning discards past training data adaptively according to its distance to the linear hyperplane, thereby improving learning efficiency. Three simulations are conducted to verify the performance of the ISVM-FC. Comparisons with fuzzy classifiers and Gaussian-kernel SVM with batch and incremental learning modes demonstrate that the ISVM-FC improves training and test times, and reduces memory consumption for classifier storage without deteriorating the generalization ability.