Search Results for author:
Found 8 papers, 0 papers with code
Vector Quantization by Minimizing Kullback-Leibler Divergence
This paper proposes a new method for vector quantization by minimizing the Kullback-Leibler Divergence between the class label distributions over the quantization inputs, which are original vectors, and the output, which is the quantization subsets of the vector set.
Supervised cross-modal factor analysis for multiple modal data classification
In this problem, each document is composed two different modals of data, i. e., an image and a text.
Supervised learning of sparse context reconstruction coefficients for data representation and classification
Context of data points, which is usually defined as the other data points in a data set, has been found to play important roles in data representation and classification.
Multiple kernel multivariate performance learning using cutting plane algorithm
The learning of the classifier parameter and the kernel weight are unified in a single objective function considering to minimize the upper boundary of the given multivariate performance measure.
Semi-supervised structured output prediction by local linear regression and sub-gradient descent
The existing semi-supervise structured output prediction methods learn a global predictor for all the data points in a data set, which ignores the differences of local distributions of the data set, and the effects to the structured output prediction.
Learning convolutional neural network to maximize Pos@Top performance measure
The CNN model is used to represent the multi-instance data point, and a classifier function is used to predict the label from the its CNN representation.
A novel image tag completion method based on convolutional neural network
In this paper, we study the problem of image tag complete and proposed a novel method for this problem based on a popular image representation method, convolutional neural network (CNN).
LDCCNLP at IJCNLP-2017 Task 2: Dimensional Sentiment Analysis for Chinese Phrases Using Machine Learning
In our system, the MAE predictive values of Valence and Arousal were 0. 811 and 0. 996, respectively, for the sentiment dimension prediction of words in Chinese.
