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ICMLA
2010
8 years 7 months ago
Boosting Multi-Task Weak Learners with Applications to Textual and Social Data
Abstract--Learning multiple related tasks from data simultaneously can improve predictive performance relative to learning these tasks independently. In this paper we propose a nov...
Jean Baptiste Faddoul, Boris Chidlovskii, Fabien T...
JAIR
2000
102views more  JAIR 2000»
8 years 9 months ago
A Model of Inductive Bias Learning
A major problem in machine learning is that of inductive bias: how to choose a learner's hypothesis space so that it is large enough to contain a solution to the problem bein...
Jonathan Baxter
AR
2004
82views more  AR 2004»
8 years 9 months ago
Multiple tasks manipulation for a robotic manipulator
Robotic manipulators can execute multiple tasks precisely at the same time and, thus, the task-priority scheme plays an important role in implementing multiple tasks. Until now, se...
Youngjin Choi, Yonghwan Oh, Sang-Rok Oh, Jonghoon ...
ICML
2010
IEEE
8 years 10 months ago
Learning Programs: A Hierarchical Bayesian Approach
We are interested in learning programs for multiple related tasks given only a few training examples per task. Since the program for a single task is underdetermined by its data, ...
Percy Liang, Michael I. Jordan, Dan Klein
FLAIRS
2004
8 years 10 months ago
Developing Task Specific Sensing Strategies Using Reinforcement Learning
Robots that can adapt and perform multiple tasks promise to be a powerful tool with many applications. In order to achieve such robots, control systems have to be constructed that...
Srividhya Rajendran, Manfred Huber
FLAIRS
2007
8 years 11 months ago
Context-Sensitive MTL Networks for Machine Lifelong Learning
Context-sensitive Multiple Task Learning, or csMTL, is presented as a method of inductive transfer that uses a single output neural network and additional contextual inputs for le...
Daniel L. Silver, Ryan Poirier
FLAIRS
2010
8 years 11 months ago
CsMTL MLP For WEKA: Neural Network Learning with Inductive Transfer
We present context-sensitive Multiple Task Learning, or csMTL as a method of inductive transfer. It uses additional contextual inputs along with other input features when learning ...
Liangliang Tu, Benjamin Fowler, Daniel L. Silver
COLT
2006
Springer
9 years 1 months ago
Online Multitask Learning
We study the problem of online learning of multiple tasks in parallel. On each online round, the algorithm receives an instance and makes a prediction for each one of the parallel ...
Ofer Dekel, Philip M. Long, Yoram Singer
ICAPR
2005
Springer
9 years 2 months ago
Discovering Predictive Variables When Evolving Cognitive Models
A non-dominated sorting genetic algorithm is used to evolve models of learning from different theories for multiple tasks. Correlation analysis is performed to identify parameters...
Peter C. R. Lane, Fernand Gobet
ECRTS
2005
IEEE
9 years 3 months ago
Scheduling Tasks with Markov-Chain Based Constraints
Markov-Chain (MC) based constraints have been shown to be an effective QoS measure for a class of real-time systems, particularly those arising from control applications. Scheduli...
Donglin Liu, Xiaobo Sharon Hu, Michael D. Lemmon, ...
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