Structured Multi-Label Learning for Text Classification – This paper proposes a new method to classify a set of images into two groups, called pairwise multi-label. The proposed learning model, named Label-Label Multi-Label Learning (LML), encodes the visual features of each image into a set of labels and the labels, respectively. The main objective is to learn which labels are similar to the data. To this end, the LML model can be designed by taking the labels as inputs, and is trained by computing the joint ranking. Since labels have importance for the classification, we design a pairwise multi-label learning method. We develop a set of two LMLs, i.e., two multi-label datasets for ImageNet, VGGNet, and ImageNet, with a combination of deep CNN and deep latent space models. The learned networks are connected in the two networks by a dual manifold, and are jointly optimized by a neural network. Through simulation experiments, we demonstrate that the network’s performance can be considerably improved compared to the prior state-of-the-art approaches and outperforms that of those using supervised learning.

Understanding ontologies is very important to us. We have an immense amount of information available. We are a part of the community of scientists trying to understand ontologies. The ontology knowledge becomes an important tool to analyse, process, and understand the ontologies. There is a big amount of information available to knowledge scientists and to other information users. We use ontologies for this purpose. The ontology knowledge is used to analyse the data. Thus, it becomes necessary to understand the ontology data. In this paper, we describe our intention to use ontology knowledge to analyze, process, analyze, and understand the ontologies.

Convex Penalized Kernel SVM

Efficient and Accurate Auto-Encoders using Min-cost Algorithms

Structured Multi-Label Learning for Text Classification

Learning Representations in Data with a Neural Network based Model for Liquor Stores

A Framework for Understanding the Effect of External Information on Online Ontology LearningUnderstanding ontologies is very important to us. We have an immense amount of information available. We are a part of the community of scientists trying to understand ontologies. The ontology knowledge becomes an important tool to analyse, process, and understand the ontologies. There is a big amount of information available to knowledge scientists and to other information users. We use ontologies for this purpose. The ontology knowledge is used to analyse the data. Thus, it becomes necessary to understand the ontology data. In this paper, we describe our intention to use ontology knowledge to analyze, process, analyze, and understand the ontologies.