Pontil. Convex multi-task feature learning. Machine Learning, 73(3):243-272, 2008.Andreas Argyriou, Theodoros Evgeniou, and Massimiliano Pontil. Convex multi-task feature learning. Machine Learning, 73(3):243-272, December 2008.A. Argyriou, T. Evgeniou, M. Pontil, Convex multi-task ...
Convex Multi-Task Feature Learning. We present a method for learning sparse representations shared across multiple tasks. This method is a generalization of the well-known singletask 1-norm r... Argyriou,Andreas,Evgeniou,... - 《Insead Working Papers Collection》 被引量: 633发表: 2007年 ...
http://scholar.google.com/scholar?q=%222008%22+Convex+Multi-task+Feature+Learning http://dl.acm.org/citation.cfm?id=1455903.1455908&preflayout=flat#citedby Quotes Author Keywords Collaborative Filtering;Inductive Transfer;Kernels;Multi-Task Learning;Regularization;Transfer Learning;Vector-Valued Functions...
The first one aims to learn a shared feature representation, which can be seen as a technical combination of the convex multitask feature learning and the convex Multiclass Maximum Margin Clustering (M3C). The second one aims to learn the task relationship, which can be seen as a combination...
In the latter case we employ the handcraft MIND-SSC feature descriptor. For the former all infered train/test segmentations for the Learn2Reg tasks can be obtained at https://cloud.imi.uni-luebeck.de/s/cgXJfjDZNNgKRZe Next we create a small config file for a new task that is similar ...
Convex multi-task feature learning learning (artificial intelligence)/ convex multitask feature learningsparse representation learningsingle-task 1-norm regularizationnonconvex regularizer... A Argyriou,T Evgeniou,M Pontil - 《Machine Learning》 被引量: 1912发表: 2008年 A modified particle swarm optimi...
A convex formulation for learning shared structures from multiple tasks We present an improved formulation (iASO) for multi-task learning based on the non-convex alternating structure optimization (ASO) algorithm, in which all tasks are related by a shared feature representation. We convert iASO, a...
When used as regularizers in convex optimization problems, these functions find application in hierarchical classification, multitask learning, and estimation of vectors with disjoint supports, among other applications. We describe a general condition for convexity, which is then used to prove the ...
In this section we provide empirical estimate of the computational time required when learning discretization by the proposed framework. As a benchmark we use the task of learning the PWL embedding for non-linear classification described in Sect. 7.3. In this case learning leads to a convex optim...
Qiao. A discriminative feature learning approach for deep face recognition. In European Conference on Computer Vision, pages 499–515. Springer, 2016. [27] K. Zhang, Z. Zhang, Z. Li, and Y. Qiao. Joint face detection and alignment using multitask cascaded convolutional net- works. IEEE ...