Pareto-Path Multi-Task Multiple Kernel Learning

A traditional and intuitively appealing Multi-Task Multiple Kernel Learning (MT-MKL) method is to optimize the sum (thus, the average) of objective functions with (partially) shared kernel function, which allows information sharing amongst tasks. We point out that the obtained solution corresponds to a single point on the Pareto Front (PF) of a Multi-Objective Optimization (MOO) problem, which considers the concurrent optimization of all task objectives involved in the Multi-Task Learning (MTL) problem. Motivated by this last observation and arguing that the former approach is heuristic, we propose a novel Support Vector Machine (SVM) MT-MKL framework, that considers an implicitly-defined set of conic combinations of task objectives. We show that solving our framework produces solutions along a path on the aforementioned PF and that it subsumes the optimization of the average of objective functions as a special case. Using algorithms we derived, we demonstrate through a series of experimental results that the framework is capable of achieving better classification performance, when compared to other similar MTL approaches.Comment: Accepted by IEEE Transactions on Neural Networks and Learning System

Literature Review For Networking And Communication Technology

Report documents the results of a literature search performed in the area of networking and communication technology

Analytical And Simulation Models For Real-time Networks

Report presents a discussion of analytical and simulation models which have been developed for the purpose of assessing/predicting the performance of local area networks (LANS) used to interconnect distributed, real-time simulation and training devices

Unifying an Introduction to Artificial Intelligence Course through Machine Learning Laboratory Experiences

This paper presents work on a collaborative project funded by the National Science Foundation that incorporates machine learning as a unifying theme to teach fundamental concepts typically covered in the introductory Artificial Intelligence courses. The project involves the development of an adaptable framework for the presentation of core AI topics. This is accomplished through the development, implementation, and testing of a suite of adaptable, hands-on laboratory projects that can be closely integrated into the AI course. Through the design and implementation of learning systems that enhance commonly-deployed applications, our model acknowledges that intelligent systems are best taught through their application to challenging problems. The goals of the project are to (1) enhance the student learning experience in the AI course, (2) increase student interest and motivation to learn AI by providing a framework for the presentation of the major AI topics that emphasizes the strong connection between AI and computer science and engineering, and (3) highlight the bridge that machine learning provides between AI technology and modern software engineering

Networking And Communications Technology Laboratory: Design/development Progress Report Submission #2

Summary of the progress made to date involving the design and development of the IST\u27s network and communications technology laboratory

Transmission Characteristics Of The 3COM Etherlink II Network [adaptor]

Report on the data transmission characteristics of the 3COM Etherlink II adapter, a high performance network interface that links and IBM PC, XT, AT, PS/2 Model 25 or 30, or compatible personal computer to IEEE 802.3 Ethernet networks