GMM-IL: Image Classification Using Incrementally Learnt, Independent Probabilistic Models for Small Sample Sizes

When deep-learning classifiers try to learn new classes through supervised learning, they exhibit catastrophic forgetting issues.In this paper we propose the Gaussian Mixture Model - Incremental Learner (GMM-IL), a novel two-stage architecture ORG CORNSTARCH that couples unsupervised visual feature learning with supervised probabilistic models to r

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Supervised Learning Perspective in Logic Mining

Creating optimal logic mining is strongly dependent on how the learning data are structured.Without optimal data structure, intelligence systems integrated into logic mining, such as an artificial neural network, tend to converge to suboptimal solution.This paper proposed a novel logic mining that integrates supervised learning via association Engi

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Exploring Klebsiella pneumoniae in Healthy Poultry Reveals High Genetic Diversity, Good Biofilm-Forming Abilities and Higher Prevalence in Turkeys Than Broilers

Klebsiella pneumoniae is a well-studied human pathogen for which antimicrobial resistant and hypervirulent clones have emerged globally.K.pneumoniae is also present in a variety of environmental niches, but currently there is a lack of knowledge on the occurrence and characteristics of K.pneumoniae from non-human sources.Certain environmental niche

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