In this study, I evaluate the performance of diagonal Confidence-Weighted (CW) online linear classification on the KDD Cup 1999 dataset for network intrusion detection systems (NIDS). This is a compatible relationship due to the large number of instances in NIDS datasets, as well as the constantly changing feature distributions. CW learning achieves approximately 92% accuracy on the KDD dataset...

Given their pervasive use, social media, such as Twitter, have become a leading source of breaking news. A key task in the automated identification of such news is the detection of novel documents from a voluminous stream of text documents in a scalable manner. Motivated by this challenge, we introduce the problem of online `1-dictionary learning where unlike traditional dictionary learning, wh...

A major challenge in Content-Based Image Retrieval (CBIR) is to bridge the semantic gap between low-level image contents and high-level semantic concepts. Although researchers have investigated a variety of retrieval techniques using different types of features and distance functions, no single best retrieval solution can fully tackle this challenge. In a real-world CBIR task, it is often highl...

Background: One of the main competencies required for enabling Nursing students to provide effective clinical care is spiritual health. The growth and development of nursing students’ spiritual health rely on strengthening their cognitive and metacognitive components. What is more associated with spirituality and spiritual health is students’ metacognition. This study aimed to investigate the e...

In this lecture we will start to study the online learning setting that was discussed briefly in the first lecture. Unlike the batch setting we have studied so far, where one is given a sample or ‘batch’ of training data and the goal is to learn from this data a model that can make accurate predictions in the future, in the online setting, learning takes place in a sequence of trials: on each t...

In this paper, we propose a novel online learning algorithm, SpCoSLAM 2.0 for spatial concepts and lexical acquisition with higher accuracy and scalability. In previous work, we proposed SpCoSLAM as an online learning algorithm based on the Rao–Blackwellized particle filter. However, this conventional algorithm had problems such as the decrease of the estimation accuracy due to the influence of...

AUC (Area under the ROC curve) is an important performance measure for applications where the data is highly imbalanced. Learning to maximize AUC performance is thus an important research problem. Using a max-margin based surrogate loss function, AUC optimization problem can be approximated as a pairwise rankSVM learning problem. Batch learning methods for solving the kernelized version of this...