Predicting the popularity of certain kinds of fruit and vegetables is NP-complete – In this paper, we describe an optimization algorithm to determine if a dataset is a dataset of trees or not. It is an NP-complete, computationally expensive algorithm, but a promising candidate to tackle the data-diversity dilemma of big datasets. Given the complexity of datasets, our method provides a framework to handle large datasets. Our method requires only simple models to predict the similarity of data, and the inference-constrained assumption of probability distributions prevents expensive inference, which can be easily accomplished by any machine-learning system. We illustrate our algorithm on the MNIST data set.

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.

Learning Strict Partial Ordered Dependency Tree

Towards the Use of Deep Networks for Sentiment Analysis

# Predicting the popularity of certain kinds of fruit and vegetables is NP-complete

Efficient Inference for Multi-View Bayesian Networks

Structured Multi-Label Learning for Text ClassificationThis 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.