1 code implementation • 13 Aug 2015 • Niloofar Yousefi, Michael Georgiopoulos, Georgios C. Anagnostopoulos
When faced with learning a set of inter-related tasks from a limited amount of usable data, learning each task independently may lead to poor generalization performance.
1 code implementation • 20 Aug 2014 • Cong Li, Michael Georgiopoulos, Georgios C. Anagnostopoulos
Traditionally, Multi-task Learning (MTL) models optimize the average of task-related objective functions, which is an intuitive approach and which we will be referring to as Average MTL.
1 code implementation • 13 Aug 2015 • Yinjie Huang, Michael Georgiopoulos, Georgios C. Anagnostopoulos
In this paper we introduce a novel hash learning framework that has two main distinguishing features, when compared to past approaches.
no code implementations • 9 Dec 2013 • Cong Li, Michael Georgiopoulos, Georgios C. Anagnostopoulos
In this paper we present two related, kernel-based Distance Metric Learning (DML) methods.
no code implementations • 11 Apr 2014 • Cong Li, Michael Georgiopoulos, Georgios C. Anagnostopoulos
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.
no code implementations • 21 Jan 2014 • Cong Li, Michael Georgiopoulos, Georgios C. Anagnostopoulos
Over the past few years, Multi-Kernel Learning (MKL) has received significant attention among data-driven feature selection techniques in the context of kernel-based learning.
no code implementations • 9 Dec 2013 • Cong Li, Michael Georgiopoulos, Georgios C. Anagnostopoulos
This paper presents a RKHS, in general, of vector-valued functions intended to be used as hypothesis space for multi-task classification.
no code implementations • 21 Feb 2019 • Yinjie Huang, Cong Li, Michael Georgiopoulos, Georgios C. Anagnostopoulos
We propose a new method for local distance metric learning based on sample similarity as side information.
no code implementations • 22 Feb 2019 • Yinjie Huang, Michael Georgiopoulos, Georgios C. Anagnostopoulos
In this paper, we propose a novel hash learning approach that has the following main distinguishing features, when compared to past frameworks.