Methods proposed in the literature for zero-shot learning (ZSL) are typically suitable for offline learning and cannot continually learn from sequential streaming data.
Zero-shot learning is a new paradigm to classify objects from classes that are not available at training time.
Recently, many researchers have attempted to improve deep learning-based object detection models, both in terms of accuracy and operational speeds.
The experimental results show that VAAKELM consistently performs better than the existing classifiers, making it a viable alternative for the OCC task.
Further, to enhance the reliability, we develop CZSL for a single head continual learning setting where task identity is revealed during the training process but not during the testing.
This privileged information is available as a feature with the dataset but only for training (not for testing).
By using two types of Graph-Embedding, 4 variants of Graph-Embedded multi-layer KRR-based one-class classifier has been presented in this paper.
In this paper, we present a multiple kernel learning approach for the One-class Classification (OCC) task and employ it for anomaly detection.
In this paper, a multi-layer architecture (in a hierarchical fashion) by stacking various Kernel Ridge Regression (KRR) based Auto-Encoder for one-class classification is proposed and is referred as MKOC.
Our proposed classifiers exhibit better performance compared to ten traditional one-class classifiers and ELM based two one-class classifiers.
This paper presents an online learning with regularized kernel based one-class extreme learning machine (ELM) classifier and is referred as online RK-OC-ELM.