Learning algorithms from the fields of artificial neural networks and machine learning, typically, do not take any costs into account or allow only costs depending on the classes of the examples that are used for learning. As an extension of class dependent costs, we consider costs that are example, i.e. feature and class dependent. We derive a costsensitive perceptron learning rule for non-sep...

of thesis entitled: Statistical Machine Learning for Data Mining and Collaborative Multimedia Retrieval Submitted by HOI, Chu Hong (Steven) for the degree of Doctor of Philosophy at The Chinese University of Hong Kong in September 2006 Statistical machine learning techniques have been widely applied in data mining and multimedia information retrieval. While traditional methods, such as supervis...

The Artificial Intelligence research field since ages has incorporated a series of novel and trend setting distinct approaches including neural networks, fuzzy logic and genetic algorithms to apply them to various problem-solving domains. Machine learning techniques such as evolutionary learning, neural networks and reinforcement learning alone are difficult to apply to board games because they...

We present an on-going work on a software package that integrates discriminative machine learning with the open source WebAnnotator system of Tannier (2012). The WebAnnotator system allows users to annotate web pages within their browser with custom tag sets. Meanwhile, we integrate the WebAnnotator system with a machine learning package which enables automatic tagging of new web pages. We hope...

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Question-Answering (QA) systems like IBM Watson are particularly challenging to design and need to cover areas including computational linguistics, information retrieval, knowledge representation and reasoning, and machine learning. ‘Exobrain’ is a Korean Research Program which aims at building such a high-performing QA system for the Korean Language. In this paper, we describe ‘Brochette’, a c...

This paper provides a review and commentary on the past, present, and future of numerical optimization algorithms in the context of machine learning applications. Through case studies on text classification and the training of deep neural networks, we discuss how optimization problems arise in machine learning and what makes them challenging. A major theme of our study is that large-scale machi...

In this paper, we evaluate the performance of machine learningbased methods for detection of phishing sites. In our previous work [1], we attempted to employ a machine learning technique to improve the detection accuracy. Our preliminary evaluation showed the AdaBoost-based detection method can achieve higher detection accuracy than the traditional detection method. Here, we evaluate the perfor...

We develop an approach to utilize anisotropic metamaterials to solve one of the fundamental problems of modern plasmonics--parasitic scattering of surface waves into free-space modes, opening the road to truly two-dimensional plasmonic optics. We illustrate the developed formalism on the examples of plasmonic refractor and plasmonic crystal, and discuss limitations of the developed technique an...

Stock market forecasting has attracted so many researchers and investors that ‎many studies have been done in this field. These studies have led to the ‎development of many predictive methods, the most widely used of which are ‎machine learning-based methods. In machine learning-based methods, loss ‎function has a key role in determining the model weights. In this study a new loss ‎function is ...