Semi-supervised incremental learning

Bouchachia, Abdelhamid; Prossegger, Markus; Duman, Hakan

doi:10.1109/fuzzy.2010.5584328

Cited by 5 publications

(6 citation statements)

References 19 publications

(12 reference statements)

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“…This corresponds to tracking drifting priors (an interested reader is referred to [Zhang and Zhou 2010]), and novelty detection (an interested reader is referred to [Markou and Singh 2003;Masud et al 2011]). Furthermore, semi-supervised drift handling techniques based on clustering (an interested reader is referred to [Aggarwal 2005;Bouchachia et al 2010]) are not discussed in this paper.…”

Section: Original Datamentioning

confidence: 99%

A survey on concept drift adaptation

et al. 2014

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Concept drift primarily refers to an online supervised learning scenario when the relation between the input data and the target variable changes over time. Assuming a general knowledge of supervised learning in this paper we characterize adaptive learning process, categorize existing strategies for handling concept drift, discuss the most representative, distinct and popular techniques and algorithms, discuss evaluation methodology of adaptive algorithms, and present a set of illustrative applications. This introduction to the concept drift adaptation presents the state of the art techniques and a collection of benchmarks for researchers, industry analysts and practitioners. The survey aims at covering the different facets of concept drift in an integrated way to reflect on the existing scattered state-of-the-art.

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Section: Original Datamentioning

confidence: 99%

A survey on concept drift adaptation

et al. 2014

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“…Hence a method that updates the model in a continual and an evolutionary manner can be used in the present study. This problem has been addressed by a few researchers such as Stauffer et al [9], Radford et al [10], Bouchachia et al [5] and Lee [2]. In this study we use some findings of Lee [2].…”

Section: Online Semi-supervised Learningmentioning

confidence: 99%

Semi-supervised Online Bayesian Network Learner for Handwritten Characters Recognition

Kunwar

Pal

Blumenstein

2014

2014 22nd International Conference on Pattern Recognition

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This work addresses the problem of creating a Bayesian Network based online semi-supervised handwritten character recognisor, which learns continuously over time to make a adaptable recognisor. The proposed method makes learning possible from a continuous inflow of a potentially unlimited amount of data without the requirement for storage. It highlights the use of unlabelled data for boosting the accuracy, especially when labelled data is scarce and expensive unlike unlabelled data. An algorithm is introduced to perform semi-supervised learning based on the combination of novel online ensemble of the Randomized Bayesian network classifiers and a novel online variant of the Expectation Maximization (EM) algorithm. We make use of a novel varying weighting factor to modulate the contribution of unlabelled data. Proposed method was evaluated using online handwritten Tamil characters from the IWFHR 2006 competition dataset.The accuracy obtained was comparable to the state of the art batch learning methods like HMM and SVMs.

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“…Though it appears similar to the RNB proposed in [13], our method uses different method to update learning parameters online (eq. (4), (5) and (6)). Besides, our method uses online bagging [3] for random selection of input and they use randomized threshold selection.…”

Section: Our Contributionsmentioning

confidence: 99%

“…Hence a method that updates the model in a continual and an evolutionary manner can be used in the present study. This problem has been addressed by a few researchers such as Stauffer et al [8], Radford et al [9], Bouchachia et al [5] and Lee [2]. In this study we use some findings of Lee [2].…”

Section: Online Semi-supervised Learningmentioning

confidence: 99%

Semi-supervised Online Learning of Handwritten Characters Using a Bayesian Classifier

Kunwar

Pal

Blumenstein

2013

2013 2nd IAPR Asian Conference on Pattern Recognition

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This paper addresses the problem of creating a handwritten character recognisor, which makes use of both labelled and unlabelled data to learn continuously over time to make the recognisor adaptable. The proposed method makes learning possible from a continuous inflow of a potentially unlimited amount of data without the requirement for storage. It highlights the use of unlabelled data for better parameter estimation, especially when labelled data is scarce and expensive unlike unlabelled data. We introduce an algorithm for learning from labelled and unlabelled samples based on the combination of novel online ensemble of the Randomized Naive Bayes classifiers and a novel incremental variant of the Expectation Maximization (EM) algorithm. We make use of a weighting factor to modulate the contribution of unlabelled data. An empirical evaluation of the proposed method on Tamil handwritten base character recognition proves efficacy of the proposed method to carry out incremental semi-supervised learning and producing accuracy comparable to state-of-the-art batch learning method. Online handwritten Tamil characters from the IWFHR 2006 competition dataset was used for evaluating the proposed method.

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Semi-supervised incremental learning

Cited by 5 publications

References 19 publications

A survey on concept drift adaptation

A survey on concept drift adaptation

Semi-supervised Online Bayesian Network Learner for Handwritten Characters Recognition

Semi-supervised Online Learning of Handwritten Characters Using a Bayesian Classifier

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