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Articles 31 - 51 of 51
Full-Text Articles in Databases and Information Systems
Online Multiple Kernel Regression, Doyen Sahoo, Steven C. H. Hoi, Bin Li
Online Multiple Kernel Regression, Doyen Sahoo, Steven C. H. Hoi, Bin Li
Research Collection School Of Computing and Information Systems
Kernel-based regression represents an important family of learning techniques for solving challenging regression tasks with non-linear patterns. Despite being studied extensively, most of the existing work suffers from two major drawbacks: (i) they are often designed for solving regression tasks in a batch learning setting, making them not only computationally inefficient and but also poorly scalable in real-world applications where data arrives sequentially; and (ii) they usually assume a fixed kernel function is given prior to the learning task, which could result in poor performance if the chosen kernel is inappropriate. To overcome these drawbacks, this paper presents a novel …
Learning Relative Similarity By Stochastic Dual Coordinate Ascent, Pengcheng Wu, Ding Yi, Peilin Zhao, Chunyan Miao, Steven C. H. Hoi
Learning Relative Similarity By Stochastic Dual Coordinate Ascent, Pengcheng Wu, Ding Yi, Peilin Zhao, Chunyan Miao, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
Learning relative similarity from pairwise instances is an important problem in machine learning and has a wide range of applications. Despite being studied for years, some existing methods solved by Stochastic Gradient Descent (SGD) techniques generally suffer from slow convergence. In this paper, we investigate the application of Stochastic Dual Coordinate Ascent (SDCA) technique to tackle the optimization task of relative similarity learning by extending from vector to matrix parameters. Theoretically, we prove the optimal linear convergence rate for the proposed SDCA algorithm, beating the well-known sublinear convergence rate by the previous best metric learning algorithms. Empirically, we conduct extensive …
Online Multi-Modal Distance Metric Learning With Application To Image Retrieval, Pengcheng Wu, Steven C. H. Hoi, Peilin Zhao, Chunyan Miao, Zhi-Yong Liu
Online Multi-Modal Distance Metric Learning With Application To Image Retrieval, Pengcheng Wu, Steven C. H. Hoi, Peilin Zhao, Chunyan Miao, Zhi-Yong Liu
Research Collection School Of Computing and Information Systems
See https://ink.library.smu.edu.sg/sis_research/2924/. Distance metric learning (DML) is an important technique to improve similarity search in content-based image retrieval. Despite being studied extensively, most existing DML approaches typically adopt a single-modal learning framework that learns the distance metric on either a single feature type or a combined feature space where multiple types of features are simply concatenated. Such single-modal DML methods suffer from some critical limitations: (i) some type of features may significantly dominate the others in the DML task due to diverse feature representations; and (ii) learning a distance metric on the combined high-dimensional feature space can be extremely …
Online Feature Selection And Its Applications, Jialei Wang, Peilin Zhao, Steven C. H. Hoi, Rong Jin
Online Feature Selection And Its Applications, Jialei Wang, Peilin Zhao, Steven C. H. Hoi, Rong Jin
Research Collection School Of Computing and Information Systems
Feature selection is an important technique for data mining. Despite its importance, most studies of feature selection are restricted to batch learning. Unlike traditional batch learning methods, online learning represents a promising family of efficient and scalable machine learning algorithms for large-scale applications. Most existing studies of online learning require accessing all the attributes/features of training instances. Such a classical setting is not always appropriate for real-world applications when data instances are of high dimensionality or it is expensive to acquire the full set of attributes/features. To address this limitation, we investigate the problem of online feature selection (OFS) in …
Libol: A Library For Online Learning Algorithms, Steven C. H. Hoi, Jialei Wang, Peilin Zhao
Libol: A Library For Online Learning Algorithms, Steven C. H. Hoi, Jialei Wang, Peilin Zhao
Research Collection School Of Computing and Information Systems
LIBOL is an open-source library for large-scale online learning, which consists of a large family of efficient and scalable state-of-the-art online learning algorithms for large- scale online classification tasks. We have offered easy-to-use command-line tools and examples for users and developers, and also have made comprehensive documents available for both beginners and advanced users. LIBOL is not only a machine learning toolbox, but also a comprehensive experimental platform for conducting online learning research.
Online Multimodal Distance Metric Learning With Application To Image Retrieval, Pengcheng Wu, Steven C. H. Hoi, Hao Xia, Peilin Zhao, Dayong Wang, Chunyan Miao
Online Multimodal Distance Metric Learning With Application To Image Retrieval, Pengcheng Wu, Steven C. H. Hoi, Hao Xia, Peilin Zhao, Dayong Wang, Chunyan Miao
Research Collection School Of Computing and Information Systems
Recent years have witnessed extensive studies on distance metric learning (DML) for improving similarity search in multimedia information retrieval tasks. Despite their successes, most existing DML methods suffer from two critical limitations: (i) they typically attempt to learn a linear distance function on the input feature space, in which the assumption of linearity limits their capacity of measuring the similarity on complex patterns in real-world applications; (ii) they are often designed for learning distance metrics on uni-modal data, which may not effectively handle the similarity measures for multimedia objects with multimodal representations. To address these limitations, in this paper, we …
Online Multi-Task Collaborative Filtering For On-The-Fly Recommender Systems, Jialei Wang, Steven C. H. Hoi, Peilin Zhao, Zhi-Yong Liu
Online Multi-Task Collaborative Filtering For On-The-Fly Recommender Systems, Jialei Wang, Steven C. H. Hoi, Peilin Zhao, Zhi-Yong Liu
Research Collection School Of Computing and Information Systems
Traditional batch model-based Collaborative Filtering (CF) approaches typically assume a collection of users' rating data is given a priori for training the model. They suffer from a common yet critical drawback, i.e., the model has to be re-trained completely from scratch whenever new training data arrives, which is clearly non-scalable for large real recommender systems where users' rating data often arrives sequentially and frequently. In this paper, we investigate a novel efficient and scalable online collaborative filtering technique for on-the-fly recommender systems, which is able to effectively online update the recommendation model from a sequence of rating observations. Specifically, we …
Cost-Sensitive Online Active Learning With Application To Malicious Url Detection, Peilin Zhao, Steven C. H. Hoi
Cost-Sensitive Online Active Learning With Application To Malicious Url Detection, Peilin Zhao, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
Malicious Uniform Resource Locator (URL) detection is an important problem in web search and mining, which plays a critical role in internet security. In literature, many existing studies have attempted to formulate the problem as a regular supervised binary classification task, which typically aims to optimize the prediction accuracy. However, in a real-world malicious URL detection task, the ratio between the number of malicious URLs and legitimate URLs is highly imbalanced, making it very inappropriate for simply optimizing the prediction accuracy. Besides, another key limitation of the existing work is to assume a large amount of training data is available, …
Student Interaction With Content In Online And Hybrid Courses: Leading Horses To The Proverbial Water, Meg Murray, Jorge Perez, Debra Geist, Alison Hedrick
Student Interaction With Content In Online And Hybrid Courses: Leading Horses To The Proverbial Water, Meg Murray, Jorge Perez, Debra Geist, Alison Hedrick
Faculty Articles
Permutations of traditional and online learning are rapidly advancing along a blended continuum, prompting conjecture that learning and e-learning will soon be indistinguishable. As variations of blended learning evolve, educators worldwide must develop better understanding of how effective interaction with course content impacts engagement and learning. This study compares patterns of access to instructional content in online and hybrid courses offered at a regional university in the United States. Frequency counts and access rates were examined for course content in four categories: core materials, direct support, indirect support, and ancillary materials. Observed results were echoed in responses to a survey …
Confidence Weighted Mean Reversion Strategy For Online Portfolio Selection, Bin Li, Steven C. H. Hoi, Peilin Zhao, Vivekanand Gopalkrishnan
Confidence Weighted Mean Reversion Strategy For Online Portfolio Selection, Bin Li, Steven C. H. Hoi, Peilin Zhao, Vivekanand Gopalkrishnan
Research Collection School Of Computing and Information Systems
Online portfolio selection has been attracting increasing attention from the data mining and machine learning communities. All existing online portfolio selection strategies focus on the first order information of a portfolio vector, though the second order information may also be beneficial to a strategy. Moreover, empirical evidence shows that relative stock prices may follow the mean reversion property, which has not been fully exploited by existing strategies. This article proposes a novel online portfolio selection strategy named Confidence Weighted Mean Reversion (CWMR). Inspired by the mean reversion principle in finance and confidence weighted online learning technique in machine learning, CWMR …
Online Multiple Kernel Classification, Steven C. H. Hoi, Rong Jin, Peilin Zhao, Tianbao Yang
Online Multiple Kernel Classification, Steven C. H. Hoi, Rong Jin, Peilin Zhao, Tianbao Yang
Research Collection School Of Computing and Information Systems
Although both online learning and kernel learning have been studied extensively in machine learning, there is limited effort in addressing the intersecting research problems of these two important topics. As an attempt to fill the gap, we address a new research problem, termed Online Multiple Kernel Classification (OMKC), which learns a kernel-based prediction function by selecting a subset of predefined kernel functions in an online learning fashion. OMKC is in general more challenging than typical online learning because both the kernel classifiers and the subset of selected kernels are unknown, and more importantly the solutions to the kernel classifiers and …
Online Multi-Modal Distance Learning For Scalable Multimedia Retrieval, Hao Xia, Pengcheng Wu, Steven C. H. Hoi
Online Multi-Modal Distance Learning For Scalable Multimedia Retrieval, Hao Xia, Pengcheng Wu, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
In many real-word scenarios, e.g., multimedia applications, data often originates from multiple heterogeneous sources or are represented by diverse types of representation, which is often referred to as "multi-modal data". The definition of distance between any two objects/items on multi-modal data is a key challenge encountered by many real-world applications, including multimedia retrieval. In this paper, we present a novel online learning framework for learning distance functions on multi-modal data through the combination of multiple kernels. In order to attack large-scale multimedia applications, we propose Online Multi-modal Distance Learning (OMDL) algorithms, which are significantly more efficient and scalable than the …
Cost-Sensitive Online Classification, Jialei Wang, Peilin Zhao, Steven C. H. Hoi
Cost-Sensitive Online Classification, Jialei Wang, Peilin Zhao, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
Both cost-sensitive classification and online learning have been extensively studied in data mining and machine learning communities, respectively. However, very limited study addresses an important intersecting problem, that is, “Cost-Sensitive Online Classification". In this paper, we formally study this problem, and propose a new framework for Cost-Sensitive Online Classification by directly optimizing cost-sensitive measures using online gradient descent techniques. Specifically, we propose two novel cost-sensitive online classification algorithms, which are designed to directly optimize two well-known cost-sensitive measures: (i) maximization of weighted sum of sensitivity and specificity, and (ii) minimization of weighted misclassification cost. We analyze the theoretical bounds of …
Virtual Phd Courses – A New Mode Of Phd Education?, Bjørn Erik Munkvold, Ilze Zigurs, Deepak Khazanchi
Virtual Phd Courses – A New Mode Of Phd Education?, Bjørn Erik Munkvold, Ilze Zigurs, Deepak Khazanchi
Information Systems and Quantitative Analysis Faculty Proceedings & Presentations
This paper presents experiences from a joint virtual PhD course for doctoral students at a Norwegian and a US university. Based on an experiential learning approach, the course focused on practices for virtual research collaboration. Through six synchronous online sessions, interspersed with interaction in sub-teams, the participants worked on developing a joint conference publication. This gave the PhD students first-hand experience with working in a virtual research team. Based on our analysis of the experiences from the course, we discuss challenges of the virtual course setting and present guidelines for the design and conduct of similar virtual courses. Our results …
On-Line Portfolio Selection With Moving Average Reversion, Bin Li, Steven C. H. Hoi
On-Line Portfolio Selection With Moving Average Reversion, Bin Li, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
On-line portfolio selection has attracted increasing interests in machine learning and AI communities recently. Empirical evidences show that stock's high and low prices are temporary and stock price relatives are likely to follow the mean reversion phenomenon. While the existing mean reversion strategies are shown to achieve good empirical performance on many real datasets, they often make the single-period mean reversion assumption, which is not always satisfied in some real datasets, leading to poor performance when the assumption does not hold. To overcome the limitation, this article proposes a multiple-period mean reversion, or so-called Moving Average Reversion (MAR), and a …
Pamr: Passive-Aggressive Mean Reversion Strategy For Portfolio Selection, Bin Li, Peilin Zhao, Steven C. H. Hoi, Vivekanand Gopalkrishnan
Pamr: Passive-Aggressive Mean Reversion Strategy For Portfolio Selection, Bin Li, Peilin Zhao, Steven C. H. Hoi, Vivekanand Gopalkrishnan
Research Collection School Of Computing and Information Systems
This project proposes a novel online portfolio selection strategy named ``Passive Aggressive Mean Reversion" (PAMR). Unlike traditional trend following approaches, the proposed approach relies upon the mean reversion relation of financial markets. Equipped with online passive aggressive learning technique from machine learning, the proposed portfolio selection strategy can effectively exploit the mean reversion property of markets. By analyzing PAMR's update scheme, we find that it nicely trades off between portfolio return and volatility risk and reflects the mean reversion trading principle. We also present several variants of PAMR algorithm, including a mixture algorithm which mixes PAMR and other strategies. We …
Collaborative Online Learning Of User Generated Content, Guangxia Li, Kuiyu Chang, Steven C. H. Hoi, Wenting Liu, Ramesh Jain
Collaborative Online Learning Of User Generated Content, Guangxia Li, Kuiyu Chang, Steven C. H. Hoi, Wenting Liu, Ramesh Jain
Research Collection School Of Computing and Information Systems
We study the problem of online classification of user generated content, with the goal of efficiently learning to categorize content generated by individual user. This problem is challenging due to several reasons. First, the huge amount of user generated content demands a highly efficient and scalable classification solution. Second, the categories are typically highly imbalanced, i.e., the number of samples from a particular useful class could be far and few between compared to some others (majority class). In some applications like spam detection, identification of the minority class often has significantly greater value than that of the majority class. Last …
Augmenting Online Learning With Real-Time Conferencing: Experiences From An International Course, Bjørn Erik Munkvold, Ilze Zigurs, Deepak Khazanchi
Augmenting Online Learning With Real-Time Conferencing: Experiences From An International Course, Bjørn Erik Munkvold, Ilze Zigurs, Deepak Khazanchi
Information Systems and Quantitative Analysis Faculty Proceedings & Presentations
This paper reports experiences from the use of real-time conferencing to support synchronous class interaction in an international online course. Through combination of video, audio, application sharing and chat, the students and instructors engaged in weekly interactions in a virtual classroom. This created an environment for rich interaction, augmenting the traditional use of course repositories. Further, this gave the students hands-on experience with real-time conferencing tools which are increasingly common in the workplace. The paper also discusses experienced challenges related to combining the use of multiple synchronous communication channels and presents implications for further use of real-time conferencing in online …
Double Updating Online Learning, Peilin Zhao, Steven C. H. Hoi, Rong Jin
Double Updating Online Learning, Peilin Zhao, Steven C. H. Hoi, Rong Jin
Research Collection School Of Computing and Information Systems
In most kernel based online learning algorithms, when an incoming instance is misclassified, it will be added into the pool of support vectors and assigned with a weight, which often remains unchanged during the rest of the learning process. This is clearly insufficient since when a new support vector is added, we generally expect the weights of the other existing support vectors to be updated in order to reflect the influence of the added support vector. In this paper, we propose a new online learning method, termed Double Updating Online Learning, or DUOL for short, that explicitly addresses this problem. …
Otl: A Framework Of Online Transfer Learning, Peilin Zhao, Steven C. H. Hoi
Otl: A Framework Of Online Transfer Learning, Peilin Zhao, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
In this paper, we investigate a new machine learning framework called Online Transfer Learning (OTL) that aims to transfer knowledge from some source domain to an online learning task on a target domain. We do not assume the target data follows the same class or generative distribution as the source data, and our key motivation is to improve a supervised online learning task in a target domain by exploiting the knowledge that had been learned from large amount of training data in source domains. OTL is in general challenging since data in both domains not only can be different in …
Collaborative Examinations In Asyncronous Learning Networks : Field Experiments On Collaborative Learning Through Online Assessments, Jia Shen
Dissertations
With the proliferation of computer networks and the emergence of virtual teams, learning and knowledge sharing in the online environment has become an increasingly important topic. Applying constructivism and collaborative learning theories to assessment, the collaborative online exam is designed featuring students' active participation in various phases of the exam process through small group activities online. A participatory online exam process is designed featuring similar procedures except that students' involvement in each phase of the exam is individual. The collaborative online exam and the participatory online exam are investigated regarding student exam study strategies, group process, exam outcomes, faculty satisfaction, …