An improved fuzzy ARTMAP and Q-learning agent model for pattern classification

Pourpanah, Farhad; Wang, Ran; Lim, Chee Peng; Seera, Manjeevan; Tan, Choo Jun

doi:10.1016/j.neucom.2019.06.002

Cited by 24 publications

(6 citation statements)

References 73 publications

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“…In addition, ensemblebased sampling [520], [521] methods have shown the capability to approximate sampling techniques (i.e., Thompson sampling) and properly deal with uncertainty in complex methods such as different NNs. Finally, quantifying uncertainties for multi-agent systems [522], [523] is also another important future direction since an individual agent cannot solve problems as are impossible or difficult.…”

Section: Natural Language Processing (Nlp)mentioning

confidence: 99%

A review of uncertainty quantification in deep learning: Techniques, applications and challenges

et al. 2021

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Section: Natural Language Processing (Nlp)mentioning

confidence: 99%

A review of uncertainty quantification in deep learning: Techniques, applications and challenges

et al. 2021

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“…ensemble models [206] and metalearning strategy [111]. Ensemble models employ a number of individual classifiers to produce multiple predictions, and the final decision is reached by combining the predictions [207]- [209]. Recently, Felix et al [73] introduced an ensemble of visual and semantic classifiers to explore the multi-modality aspect of GZSL.…”

Section: Research Gapsmentioning

confidence: 99%

A Review of Generalized Zero-Shot Learning Methods

Pourpanah

Abdar

Luo

et al. 2022

IEEE Trans. Pattern Anal. Mach. Intell.

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Generalized zero-shot learning (GZSL) aims to train a model for classifying data samples under the condition that some output classes are unknown during supervised learning. To address this challenging task, GZSL leverages semantic information of the seen (source) and unseen (target) classes to bridge the gap between both seen and unseen classes. Since its introduction, many GZSL models have been formulated. In this review paper, we present a comprehensive review on GZSL. Firstly, we provide an overview of GZSL including the problems and challenges. Then, we introduce a hierarchical categorization for the GZSL methods and discuss the representative methods in each category. In addition, we discuss the available benchmark data sets and applications of GZSL, along with a discussion on the research gaps and directions for future investigations.

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“…Incremental learning refers to the condition of continuous model adaptation based on a constantly arriving input samples [15][16][17]. Unlike machine learning techniques with batch learning procedure that have to re-execute an iterative training procedure using both old and new samples, incremental learning techniques require to learn only new samples without re-learning preciously learned samples [18,19].…”

Section: Category 2: Incremental Learningmentioning

confidence: 99%