We experimentally show that we are up to 45% more accurate than HC in terms of clustering accuracy.
We develop a Vector Quantized Spectral Clustering (VQSC) algorithm that is a combination of Spectral Clustering (SC) and Vector Quantization (VQ) sampling for grouping Soybean genomes.
In this paper, we present a multiple kernel learning approach for the One-class Classification (OCC) task and employ it for anomaly detection.
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.
We apply our two new descriptors on all images of the IRMA database for density wise classification, and compare with the standard descriptors.