31 citations · 41 across the 7 of their papers we have counts for
7 papers
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…
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…