most citedRadial basis function kernel optimization for Support Vector Machine classifiers

14 citations · 22 across the 5 of their papers we have counts for

collaborators

5 papers

eess.IV2021

A Real Use Case of Semi-Supervised Learning for Mammogram Classification in a Local Clinic of Costa Rica

Saul Calderon-Ramirez, Diego Murillo-Hernandez, Kevin Rojas-Salazar +3

The implementation of deep learning based computer aided diagnosis systems for the classification of mammogram images can help in improving the accuracy, reliability, and cost of d…

cs.CV2021

Enforcing Morphological Information in Fully Convolutional Networks to Improve Cell Instance Segmentation in Fluorescence Microscopy Images

Willard Zamora-Cardenas, Mauro Mendez, Saul Calderon-Ramirez +5

Cell instance segmentation in fluorescence microscopy images is becoming essential for cancer dynamics and prognosis. Data extracted from cancer dynamics allows to understand and a…

eess.IV20202 cited

Correcting Data Imbalance for Semi-Supervised Covid-19 Detection Using X-ray Chest Images

Saul Calderon-Ramirez, Shengxiang-Yang, Armaghan Moemeni +4

The Corona Virus (COVID-19) is an internationalpandemic that has quickly propagated throughout the world. The application of deep learning for image classification of chest X-ray i…

cs.LG202014 cited

Radial basis function kernel optimization for Support Vector Machine classifiers

Karl Thurnhofer-Hemsi, Ezequiel López-Rubio, Miguel A. Molina-Cabello +1

Support Vector Machines (SVMs) are still one of the most popular and precise classifiers. The Radial Basis Function (RBF) kernel has been used in SVMs to separate among classes wit…

cs.LG20206 cited

MixMOOD: A systematic approach to class distribution mismatch in semi-supervised learning using deep dataset dissimilarity measures

Saul Calderon-Ramirez, Luis Oala, Jordina Torrents-Barrena +4

In this work, we propose MixMOOD - a systematic approach to mitigate effect of class distribution mismatch in semi-supervised deep learning (SSDL) with MixMatch. This work is divid…