Viral Genome Deep Classifier

A Deep Learning Interpretable Classifier for Diabetic Retinopathy Disease Grading

Deep neural network models have been proven to be very successful in image classification tasks, also for medical diagnosis, but their main concern is its lack of interpretability. They use to work as intuition machines with high statistical confidence but unable to give interpretable explanations about the reported results. The vast amount of parameters of these models make difficult to infer ...

Analyzed Performance for a Chairs Classifier through Deep Learning

Deep learning is a branch of machine learning and this technique allows us to create classifiers. We must find the best dataset size for a classifier process to permit using less time and give good accuracy. In this paper we will propose models with different deep layers and size dimensions for detecting the best model to solve a task that needs quick time processing.

Deep Classifier for Large Scale Hierarchical Text Classification

In this competition, we refined a novel algorithm for classification on large scale documents with deep category structure based on a two-stage strategy known as the deep-classifier [1]. The basic idea of deep-classifier is to take advantage of the better performance of kNN relevance search on large scale categories and the higher precision of the Naive Bayes for multi-class classification. As ...

A Wavelet Deep Belief Network-Based Classifier for Medical Images

Deep Learning of Scene-Specific Classifier for Pedestrian Detection

The performance of a detector depends much on its training dataset and drops significantly when the detector is applied to a new scene due to the large variations between the source training dataset and the target scene. In order to bridge this appearance gap, we propose a deep model to automatically learn scene-specific features and visual patterns in static video surveillance without any manu...