We propose a new active learning method for classification, which handles label noise
without relying on multiple oracles (ie, crowdsourcing). We propose a strategy that selects …

Support vector machine (SVM) is a comparatively new machine learning algorithm for
classification, while logistic regression (LR) is an old standard statistical classification …

Deep neural network (DNN) as a popular machine learning model is found to be vulnerable
to adversarial attack. This attack constructs adversarial examples by adding small …

Pool-based active learning is a paradigm where users (eg, domains experts) are iteratively
asked to label initially unlabeled data, eg, to train a classifier from these data. An appropriate …

This study addresses the time series forecasting performance of sparsely connected neural
networks (SCNNs). A novel type of SCNNs is presented based on the Apollonian networks …

This paper proposes a robust method of dimension reduction using fuzzy rough sets, in
which the reduction results can reflect the reducts obtained on all of the possible …

Abstract English part-of-speech taggers regularly make egregious errors related to noun-
verb ambiguity, despite having achieved 97%+ accuracy on the WSJ Penn Treebank since …

A major constraint of machine learning techniques for solving several information extraction
problems is the availability of sufficient amount of training examples, which involve huge …

This paper establishes the equivalent relationship between the data classification machine
and the data envelopment analysis (DEA) model, and thus build up a DEA based …

Insurance organisations store voluminous textual data sources on a daily basis (free text
fields used by telephonists, emails, customer reviews,...). However, this mass of textual data …