Book ChapterDOI
Integrated Semi-Supervised Model for Learning and Classification
Vandna Bhalla,Santanu Chaudhury +1 more
- pp 183-195
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TLDR
This work proposes a novel framework where the small labelled dataset is appropriately augmented using the intelligent learning mechanisms of artificial immune systems to train the proposed model and shows that the generative deep framework utilizing artificial immune system principles provides a highly competitive approach for learning in the semi-supervised environment.Abstract:
Labelled data are not only time consuming but often expensive and difficult to procure as it involves skilful inputs by humans to tag and annotate. Contrary to this unlabelled data is comparatively easier to procure but fewer methods exist to optimally use them. Semi-Supervised Learning overcomes this problem and assists to build better classifiers by using unlabelled data along with sufficient labelled data and may actually yield higher accuracy with considerably less human input effort. But if the labelled data set is inadequate in size then the Semi-Supervised techniques are also stuck. We propose a novel framework where the small labelled dataset is appropriately augmented using the intelligent learning mechanisms of artificial immune systems to train the proposed model. The model retrains with the unlabelled data to fortify the learning mechanism. We show that the generative deep framework utilizing artificial immune system principles provides a highly competitive approach for learning in the semi-supervised environment.read more
References
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Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising 1 criterion
TL;DR: This work clearly establishes the value of using a denoising criterion as a tractable unsupervised objective to guide the learning of useful higher level representations.
Journal Article
Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion
TL;DR: Denoising autoencoders as mentioned in this paper are trained locally to denoise corrupted versions of their inputs, which is a straightforward variation on the stacking of ordinary autoencoder.
Journal ArticleDOI
Learning and optimization using the clonal selection principle
L.N. de Castro,F.J. Von Zuben +1 more
TL;DR: This paper proposes a computational implementation of the clonal selection principle that explicitly takes into account the affinity maturation of the immune response and derives two versions of the algorithm, derived primarily to perform machine learning and pattern recognition tasks.
Posted Content
Semi-Supervised Learning with Deep Generative Models
TL;DR: It is shown that deep generative models and approximate Bayesian inference exploiting recent advances in variational methods can be used to provide significant improvements, making generative approaches highly competitive for semi-supervised learning.
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