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TCAD
2010
103views more  TCAD 2010»
8 years 6 months ago
Supervised Learning Based Power Management for Multicore Processors
- This paper presents a supervised learning based power management framework for a multi-processor system, where a power manager (PM) learns to predict the system performance state...
Hwisung Jung, Massoud Pedram
JMLR
2010
140views more  JMLR 2010»
8 years 6 months ago
Learning From Crowds
For many supervised learning tasks it may be infeasible (or very expensive) to obtain objective and reliable labels. Instead, we can collect subjective (possibly noisy) labels fro...
Vikas C. Raykar, Shipeng Yu, Linda H. Zhao, Gerard...
JMLR
2010
102views more  JMLR 2010»
8 years 6 months ago
Unsupervised Supervised Learning I: Estimating Classification and Regression Errors without Labels
Estimating the error rates of classifiers or regression models is a fundamental task in machine learning which has thus far been studied exclusively using supervised learning tech...
Pinar Donmez, Guy Lebanon, Krishnakumar Balasubram...

Publication
400views
8 years 6 months ago
Partitioning Histopathological Images: An Integrated Framework for Supervised Color-Texture Segmentation and Cell Splitting
For quantitative analysis of histopathological images, such as the lymphoma grading systems, quantification of features is usually carried out on single cells before categorizing...
Hui Kong, Metin Gurcan, and Kamel Belkacem-Boussai...
PRL
2011
8 years 6 months ago
Object recognition using proportion-based prior information: Application to fisheries acoustics
: This paper addresses the inference of probabilistic classification models using weakly supervised learning. The main contribution of this work is the development of learning meth...
Riwal Lefort, Ronan Fablet, Jean-Marc Boucher
HAIS
2010
Springer
8 years 9 months ago
Reducing Dimensionality in Multiple Instance Learning with a Filter Method
In this article, we describe a feature selection algorithm which can automatically find relevant features for multiple instance learning. Multiple instance learning is considered a...
Amelia Zafra, Mykola Pechenizkiy, Sebastián...
COLT
2010
Springer
8 years 9 months ago
Learning to Create is as Hard as Learning to Appreciate
We explore the relationship between a natural notion of unsupervised learning studied by Kearns et al. (STOC '94), which we call here "learning to create" (LTC), an...
David Xiao
IJON
2008
133views more  IJON 2008»
8 years 10 months ago
A multi-objective approach to RBF network learning
The problem of inductive supervised learning is discussed in this paper within the context of multi-objective (MOBJ) optimization. The smoothness-based apparent (effective) comple...
Illya Kokshenev, Antônio de Pádua Bra...
ML
2002
ACM
178views Machine Learning» more  ML 2002»
8 years 11 months ago
Metric-Based Methods for Adaptive Model Selection and Regularization
We present a general approach to model selection and regularization that exploits unlabeled data to adaptively control hypothesis complexity in supervised learning tasks. The idea ...
Dale Schuurmans, Finnegan Southey
AIM
1999
8 years 11 months ago
Machine Learning, Machine Vision, and the Brain
The problem of learning is arguably at the very core of the problem of intelligence, both biological and artificial. In this paper we review our work over the last ten years in th...
Tomaso Poggio, Christian R. Shelton
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