Maximum likelihood estimation for multivariate mixture observations of Markov chins
IEEE Transactions on Information Theory
Adaptive algorithms and stochastic approximations
Adaptive algorithms and stochastic approximations
Smooth on-line learning algorithms for hidden Markov models
Neural Computation
Exact adaptive filters for Markov chains observed in Gaussian noise
Automatica (Journal of IFAC)
Exponentiated gradient versus gradient descent for linear predictors
Information and Computation
A view of the EM algorithm that justifies incremental, sparse, and other variants
Learning in graphical models
An HMM-Based Approach for Off-Line Unconstrained Handwritten Word Modeling and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Training Hidden Markov Models with Multiple Observations-A Combinatorial Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discovery and Segmentation of Activities in Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
Spoken Language Processing: A Guide to Theory, Algorithm, and System Development
Spoken Language Processing: A Guide to Theory, Algorithm, and System Development
A Probabilistic Background Model for Tracking
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
A Unifeid Bias-Variance Decomposition and its Applications
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Analysis and Synthesis of Agents That Learn from Distributed Dynamic Data Sources
Emergent Neural Computational Architectures Based on Neuroscience - Towards Neuroscience-Inspired Computing
On-Line Estimation of Hidden Markov Model Parameters
DS '00 Proceedings of the Third International Conference on Discovery Science
Local Gain Adaptation in Stochastic Gradient Descent
Local Gain Adaptation in Stochastic Gradient Descent
A comparison of SVM and HMM classifiers in the off-line signature verification
Pattern Recognition Letters
Solvability of a Markovian Model of an IEEE 802.11 LAN under a Backoff Attack
MASCOTS '05 Proceedings of the 13th IEEE International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems
Convergence Theorems for Generalized Alternating Minimization Procedures
The Journal of Machine Learning Research
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Computational issues in parameter estimation for stationary hidden Markov models
Computational Statistics
Direct maximization of the likelihood of a hidden Markov model
Computational Statistics & Data Analysis
Online learning with hidden markov models
Neural Computation
Incremental estimation of discrete hidden Markov models based on a new backward procedure
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Incremental learning of statistical motion patterns with growing hidden Markov models
IEEE Transactions on Intelligent Transportation Systems
On the memory complexity of the forward-backward algorithm
Pattern Recognition Letters
A simple and efficient hidden Markov model scheme for host- based anomaly intrusion detection
IEEE Network: The Magazine of Global Internetworking - Special issue title on recent developments in network intrusion detection
Kernel Adaptive Filtering: A Comprehensive Introduction
Kernel Adaptive Filtering: A Comprehensive Introduction
Adaptive ROC-based ensembles of HMMs applied to anomaly detection
Pattern Recognition
A Maximum Likelihood Approach to Continuous Speech Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive estimation of HMM transition probabilities
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
On adaptive HMM state estimation
IEEE Transactions on Signal Processing
On-line identification of hidden Markov models via recursiveprediction error techniques
IEEE Transactions on Signal Processing
Learn++: an incremental learning algorithm for supervised neuralnetworks
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
On the relations between modeling approaches for speech recognition
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
On receiver structures for channels having memory
IEEE Transactions on Information Theory
Maximum likelihood estimation for multivariate observations of Markov sources
IEEE Transactions on Information Theory
Recursive estimation in mixture models with Markov regime
IEEE Transactions on Information Theory
Graphical EM for on-line learning of grammatical probabilities in radar Electronic Support
Applied Soft Computing
Information Sciences: an International Journal
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The performance of Hidden Markov Models (HMMs) targeted for complex real-world applications are often degraded because they are designed a priori using limited training data and prior knowledge, and because the classification environment changes during operations. Incremental learning of new data sequences allows to adapt HMM parameters as new data becomes available, without having to retrain from the start on all accumulated training data. This paper presents a survey of techniques found in literature that are suitable for incremental learning of HMM parameters. These techniques are classified according to the objective function, optimization technique and target application, involving block-wise and symbol-wise learning of parameters. Convergence properties of these techniques are presented along with an analysis of time and memory complexity. In addition, the challenges faced when these techniques are applied to incremental learning is assessed for scenarios in which the new training data is limited and abundant. While the convergence rate and resource requirements are critical factors when incremental learning is performed through one pass over abundant stream of data, effective stopping criteria and management of validation sets are important when learning is performed through several iterations over limited data. In both cases managing the learning rate to integrate pre-existing knowledge and new data is crucial for maintaining a high level of performance. Finally, this paper underscores the need for empirical benchmarking studies among techniques presented in literature, and proposes several evaluation criteria based on non-parametric statistical testing to facilitate the selection of techniques given a particular application domain.