31 citations · 61 across the 12 of their papers we have counts for
11 papers · 1 filter
Learning Hash Function through Codewords
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. First, the codewords are utilized in…
Reduced-Rank Local Distance Metric Learning for k-NN Classification
YInjie Huang, Cong Li, Michael Georgiopoulos +1
We propose a new method for local distance metric learning based on sample similarity as side information. These local metrics, which utilize conical combinations of metric weight…
Multi-Task Learning Using Neighborhood Kernels
Niloofar Yousefi, Cong Li, Mansooreh Mollaghasemi +2
This paper introduces a new and effective algorithm for learning kernels in a Multi-Task Learning (MTL) setting. Although, we consider a MTL scenario here, our approach can be easi…
Local Rademacher Complexity-based Learning Guarantees for Multi-Task Learning
Niloofar Yousefi, Yunwen Lei, Marius Kloft +2
We show a Talagrand-type concentration inequality for Multi-Task Learning (MTL), using which we establish sharp excess risk bounds for MTL in terms of distribution- and data-depend…
Hash Function Learning via Codewords
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. First, it utilizes codewords in the Hamming…
Multi-Task Learning with Group-Specific Feature Space Sharing
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. Multi-Task…
Conic Multi-Task Classification
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 Averag…
Pareto-Path Multi-Task Multiple Kernel Learning
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) share…
A Unifying Framework for Typical Multi-Task Multiple Kernel Learning Problems
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. MKL…
Multi-Task Classification Hypothesis Space with Improved Generalization Bounds
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. It extends similar hypothesis spaces that…
Kernel-based Distance Metric Learning in the Output Space
Cong Li, Michael Georgiopoulos, Georgios C. Anagnostopoulos
In this paper we present two related, kernel-based Distance Metric Learning (DML) methods. Their respective models non-linearly map data from their original space to an output spac…