Transfer learning has been demonstrated to be effective for many real-world applications as
it exploits knowledge present in labeled training data from a source domain to enhance a …

Stochastic Gradient Descent and Its Variants in Machine Learning | Journal of the Indian Institute
of Science Skip to main content Springer Nature Link Account Menu Find a journal Publish with …

Intrusion detection ensures that IoT can protect itself against malicious intrusions in
extensive and intricate network traffic data. In recent years, deep learning has been …

Visual tracking, in essence, deals with non-stationary image streams that change over time.
While most existing algorithms are able to track objects well in controlled environments, they …

We present a new technique for extracting line‐type features on point‐sampled geometry.
Given an unstructuredpoint cloud as input, our method first applies principal component …

In today's industrial systems, economic markets, life and health-care sciences fuzzy systems
play an important role in many application scenarios such as system identification, fault …

As a widely used approach for feature extraction and data reduction, Principal Components
Analysis (PCA) suffers from high computational cost, large memory requirement and low …

As a challenging problem, few-shot class-incremental learning (FSCIL) continually learns a
sequence of tasks, confronting the dilemma between slow forgetting of old knowledge and …

This second edition of the must-read work in the field presents generic computational
models and techniques that can be used for the development of evolving, adaptive modeling …

This paper presents a constructive method for deriving an updated discriminant eigenspace
for classification when bursts of data that contains new classes is being added to an initial …