This paper presents a novel semisupervised learning algorithm called Active Deep Networks (ADN), to address the semi-supervised sentiment classification problem with active learning. First, we propose the semi-supervised learning method of ADN. ADN is constructed by Restricted Boltzmann Machines (RBM) with unsupervised learning using labeled data and abundant of unlabeled data. Then the constru...

Arabic dialect classification has been an important and challenging problem for Arabic language processing, especially for social media text analysis and machine translation. In this paper we propose an approach to improving Arabic dialect classification with semi-supervised learning: multiple classifiers are trained with weakly supervised, strongly supervised, and unsupervised data. Their comb...

This paper introduces a novel approach to leverage features learned from both supervised and self-supervised paradigms, improve image classification tasks, specifically for vehicle classification. Two state-of-the-art learning methods, DINO data2vec, were evaluated compared their representation of images. The former contrasts local global views while the latter uses masked prediction on multi-l...

This paper presents an extension of the Self Organizing Map model called Associative SOM that is able to process different types of input data in separated data-paths. The ASOM model can easily deal with situations of incomplete data-patterns and incorporate class labels for supervisory purposes. The ASOM is successfully compared with Multilayer Perceptrons in the incremental classification of ...

Let X be a random variable taking values in a Hilbert space and let Y be a random label with values in {0, 1}. Given a collection of classification rules and a learning sample of independent copies of the pair (X, Y ), it is shown how to select optimally and consistently a classifier. As a general strategy, the learning sample observations are first expanded on a wavelet basis and the overall i...

Most semi-supervised learning algorithms have been designed for binary classification, and are extended to multi-class classification by approaches such as one-against-the-rest. The main shortcoming of these approaches is that they are unable to exploit the fact that each example is only assigned to one class. Additional problems with extending semisupervised binary classifiers to multi-class p...

This document discusses Bayesian classification in the context of supervised learning. Supervised learning is defined. An approach is described in which feature likelihooods are estimated from data, and then classification is done by computing class posteriors given features using Bayes rule. Estimating of feature likelihoods, independence of features, quantization of features, and information ...

In this paper, we describe a new method for the problem of named entity classification for specialized or technical domains, using distant supervision. Our approach relies on a simple observation: in some specialized domains, named entities are almost unambiguous. Thus, given a seed list of names of entities, it is cheap and easy to obtain positive examples from unlabeled texts using a simple s...

Most text categorization methods require text content of documents that is often difficult to obtain. We consider “Collaborative Text Categorization”, where each document is represented by the feedback from a large number of users. Our study focuses on the semisupervised case in which one key challenge is that a significant number of users have not rated any labeled document. To address this pr...

Dietterich (1998) reviews five statistical tests and proposes the 5 × 2 cv t test for determining whether there is a significant difference between the error rates of two classifiers. In our experiments, we noticed that the 5× 2 cv t test result may vary depending on factors that should not affect the test, and we propose a variant, the combined 5×2 cv F test, that combines multiple statistics ...