Neocognitron capable of incremental learning
Neural Networks
IDR/QR: an incremental dimension reduction algorithm via QR decomposition
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
IDR/QR: An Incremental Dimension Reduction Algorithm via QR Decomposition
IEEE Transactions on Knowledge and Data Engineering
Ordered incremental training for GA-based classifiers
Pattern Recognition Letters
A cooperative constructive method for neural networks for pattern recognition
Pattern Recognition
International Journal of Hybrid Intelligent Systems
A framework for generating data to simulate changing environments
AIAP'07 Proceedings of the 25th conference on Proceedings of the 25th IASTED International Multi-Conference: artificial intelligence and applications
Improving the performance of an incremental algorithm driven by error margins
Intelligent Data Analysis - Knowledge Discovery from Data Streams
Incremental Learning and Its Application to Bushing Condition Monitoring
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
Supervised Incremental Learning with the Fuzzy ARTMAP Neural Network
ANNPR '08 Proceedings of the 3rd IAPR workshop on Artificial Neural Networks in Pattern Recognition
Open-ended category learning for language acquisition
Connection Science - Language and Robots
Pattern Recognition Letters
Combining Online Classification Approaches for Changing Environments
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
A Parallel Incremental Learning Algorithm for Neural Networks with Fault Tolerance
High Performance Computing for Computational Science - VECPAR 2008
An incremental learning algorithm for function approximation
Advances in Engineering Software
Negative correlation in incremental learning
Natural Computing: an international journal
Weights Updated Voting for Ensemble of Neural Networks Based Incremental Learning
ISNN '09 Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks
Evolving logic networks with real-valued inputs for fast incremental learning
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on human computing
IEEE Transactions on Neural Networks
Constructing ensembles of classifiers by means of weighted instance selection
IEEE Transactions on Neural Networks
An Adaptive Learning Algorithm for Supervised Neural Network with Contour Preserving Classification
AICI '09 Proceedings of the International Conference on Artificial Intelligence and Computational Intelligence
SERA: selectively recursive approach towards nonstationary imbalanced stream data mining
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Using diversity to handle concept drift in on-line learning
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Towards incremental classifier fusion
Intelligent Data Analysis
A comparison of techniques for on-line incremental learning of HMM parameters in anomaly detection
CISDA'09 Proceedings of the Second IEEE international conference on Computational intelligence for security and defense applications
Incremental adaptation of fuzzy ARTMAP neural networks for video-based face classification
CISDA'09 Proceedings of the Second IEEE international conference on Computational intelligence for security and defense applications
Semi-supervised learning approaches for predicting semantic characteristics of lung nodules
Intelligent Decision Technologies - Special issue on advances in medical intelligent decision support systems
Random feature subset selection for ensemble based classification of data with missing features
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
An ensemble approach for incremental learning in nonstationary environments
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
An ensemble approach for data fusion with learn++
MCS'03 Proceedings of the 4th international conference on Multiple classifier systems
Incremental learning of support vector machines by classifier combining
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Confidence estimation using the incremental learning algorithm, learn
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Auto-adaptive and dynamical clustering neural network
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Probabilistic aggregation of classifiers for incremental learning
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Incremental learning with multiple classifier systems using correction filters for classification
IDA'07 Proceedings of the 7th international conference on Intelligent data analysis
Context cells: towards lifelong learning in activity recognition systems
EuroSSC'09 Proceedings of the 4th European conference on Smart sensing and context
A new online learning algorithm for structure-adjustable extreme learning machine
Computers & Mathematics with Applications
Learn++.MF: A random subspace approach for the missing feature problem
Pattern Recognition
MultiFusion: A boosting approach for multimedia fusion
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Portfolio theory of multimedia fusion
Proceedings of the international conference on Multimedia
Rough set-based incremental learning approach to face recognition
RSCTC'10 Proceedings of the 7th international conference on Rough sets and current trends in computing
Online error correcting output codes
Pattern Recognition Letters
Incremental learning by heterogeneous bagging ensemble
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications - Volume Part II
Reducing the effect of out-voting problem in ensemble based incremental support vector machines
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
Incremental learning with multi-level adaptation
Neurocomputing
Journal of Biomedical Informatics
Adaptive ROC-based ensembles of HMMs applied to anomaly detection
Pattern Recognition
Incremental Boolean combination of classifiers
MCS'11 Proceedings of the 10th international conference on Multiple classifier systems
Finding reliable recommendations for trust model
WISE'06 Proceedings of the 7th international conference on Web Information Systems
Adaptive classifier selection based on two level hypothesis tests for incremental learning
SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part I
Classification of volatile organic compounds with incremental SVMs and RBF networks
ISCIS'05 Proceedings of the 20th international conference on Computer and Information Sciences
Can adaboost.m1 learn incrementally? a comparison to learn++ under different combination rules
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
Ensemble of SVMs for incremental learning
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
Ensemble confidence estimates posterior probability
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part I
An adaptive classification system for video-based face recognition
Information Sciences: an International Journal
A survey of techniques for incremental learning of HMM parameters
Information Sciences: an International Journal
An ensemble method for incremental classification in stationary and non-stationary environments
CIARP'11 Proceedings of the 16th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
An instance-window based classification algorithm for handling gradual concept drifts
ADMI'11 Proceedings of the 7th international conference on Agents and Data Mining Interaction
Incremental threshold learning for classifier selection
Neurocomputing
Graphical EM for on-line learning of grammatical probabilities in radar Electronic Support
Applied Soft Computing
An incremental neural network with a reduced architecture
Neural Networks
A new method of mining data streams using harmony search
Journal of Intelligent Information Systems
A novel local patch framework for fixing supervised learning models
Proceedings of the 21st ACM international conference on Information and knowledge management
Toward the scalability of neural networks through feature selection
Expert Systems with Applications: An International Journal
GOFAM: a hybrid neural network classifier combining fuzzy ARTMAP and genetic algorithm
Artificial Intelligence Review
Dynamic multi-objective evolution of classifier ensembles for video face recognition
Applied Soft Computing
A survey on concept drift adaptation
ACM Computing Surveys (CSUR)
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Weighted Online Sequential Extreme Learning Machine for Class Imbalance Learning
Neural Processing Letters
An incremental learning preprocessor for feed-forward neural network
Artificial Intelligence Review
The CART decision tree for mining data streams
Information Sciences: an International Journal
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We introduce Learn++, an algorithm for incremental training of neural network (NN) pattern classifiers. The proposed algorithm enables supervised NN paradigms, such as the multilayer perceptron (MLP), to accommodate new data, including examples that correspond to previously unseen classes. Furthermore, the algorithm does not require access to previously used data during subsequent incremental learning sessions, yet at the same time, it does not forget previously acquired knowledge. Learn++ utilizes ensemble of classifiers by generating multiple hypotheses using training data sampled according to carefully tailored distributions. The outputs of the resulting classifiers are combined using a weighted majority voting procedure. We present simulation results on several benchmark datasets as well as a real-world classification task. Initial results indicate that the proposed algorithm works rather well in practice. A theoretical upper bound on the error of the classifiers constructed by Learn++ is also provided