Classification-Regression for Chart Comprehension

Classification of Chart Images

Logistic regression for graph classification

In this paper we deal with graph classification. We propose a new algorithm for performing sparse logistic regression on graphs. Sparsity is required for the reason of interpretability, which is often necessary in domains such as bioinformatics or chemoinformatics. Our method is comparable in accuracy with other methods of graph classification and produces probabilistic output in addition.

Penalizied Logistic Regression for Classification

Investigation for using different penalty functions (L1 absolute value penalty or lasso, L2 standard weight decay or ridge regression, weight elimination etc.) on the weights for logistic regression for classification. 5 data sets from UCI Machine Learning Repository were used.

Tests for Classification and Regression ∗ ∗

We address the problem of computing various types of expressive tests for decision trees and regression trees. Using expressive tests is promising, because it may improve the prediction accuracy of trees, and it may also provide us some hints on scientific discovery. The drawback is that computing an optimal test could be costly. We present a unified framework to approach this problem, and we r...

Convolutional Neural Network Based Chart Image Classification

Charts are frequently embedded objects in digital documents and are used to convey a clear analysis of research results or commercial data trends. These charts are created through different means and may be represented by a variety of patterns such as column charts, line charts and pie charts. Chart recognition is as important as text recognition to automatically comprehend the knowledge within...