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Found 11 papers, 3 papers with code
A Multi-criteria Approach for Fast and Outlier-aware Representative Selection from Manifolds
The problem of representative selection amounts to sampling few informative exemplars from large datasets.
Learning Hash Function through Codewords
In this paper, we propose a novel hash learning approach that has the following main distinguishing features, when compared to past frameworks.
Reduced-Rank Local Distance Metric Learning for k-NN Classification
We propose a new method for local distance metric learning based on sample similarity as side information.
Multi-Task Learning Using Neighborhood Kernels
As shown by our empirical results, our algorithm consistently outperforms the traditional kernel learning algorithms such as uniform combination solution, convex combinations of base kernels as well as some kernel alignment-based models, which have been proven to give promising results in the past.
Multi-Task Learning with Group-Specific Feature Space Sharing
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.
Hash Function Learning via Codewords
In this paper we introduce a novel hash learning framework that has two main distinguishing features, when compared to past approaches.
Conic Multi-Task Classification
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.
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.
A Unifying Framework for Typical Multi-Task Multiple Kernel Learning Problems
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.
Multi-Task Classification Hypothesis Space with Improved Generalization Bounds
This paper presents a RKHS, in general, of vector-valued functions intended to be used as hypothesis space for multi-task classification.
Kernel-based Distance Metric Learning in the Output Space
In this paper we present two related, kernel-based Distance Metric Learning (DML) methods.
