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JMLR
2012
9 years 10 months ago
Online Incremental Feature Learning with Denoising Autoencoders
While determining model complexity is an important problem in machine learning, many feature learning algorithms rely on cross-validation to choose an optimal number of features, ...
Guanyu Zhou, Kihyuk Sohn, Honglak Lee
JMLR
2012
9 years 10 months ago
Multi-label Subspace Ensemble
A challenging problem of multi-label learning is that both the label space and the model complexity will grow rapidly with the increase in the number of labels, and thus makes the...
Tianyi Zhou, Dacheng Tao
ICCV
2011
IEEE
10 years 8 months ago
Articulated Part-based Model for Joint Object Detection and Pose Estimation
Despite recent successes, pose estimators are still somewhat fragile, and they frequently rely on a precise knowledge of the location of the object. Unfortunately, articulated obj...
Min Sun, Silvio Savarese
SIGIR
2011
ACM
10 years 11 months ago
Fast context-aware recommendations with factorization machines
The situation in which a choice is made is an important information for recommender systems. Context-aware recommenders take this information into account to make predictions. So ...
Steffen Rendle, Zeno Gantner, Christoph Freudentha...
IJON
2002
84views more  IJON 2002»
11 years 7 months ago
On the generative probability density model in the self-organizing map
The Self-Organizing Map, SOM, is a widely used tool in exploratory data analysis. A major drawback of the SOM has been the lack of a theoretically justified criterion for model se...
Timo Kostiainen, Jouko Lampinen
ICANN
2009
Springer
12 years 2 months ago
Measuring and Optimizing Behavioral Complexity for Evolutionary Reinforcement Learning
Model complexity is key concern to any artificial learning system due its critical impact on generalization. However, EC research has only focused phenotype structural complexity ...
Faustino J. Gomez, Julian Togelius, Jürgen Sc...
CRV
2009
IEEE
115views Robotics» more  CRV 2009»
12 years 2 months ago
Learning Model Complexity in an Online Environment
In this paper we introduce the concept and method for adaptively tuning the model complexity in an online manner as more examples become available. Challenging classification pro...
Dan Levi, Shimon Ullman
ICCV
2003
IEEE
12 years 10 months ago
Controlling Model Complexity in Flow Estimation
This paper describes a novel application of Statistical Learning Theory (SLT) to control model complexity in flow estimation. SLT provides analytical generalization bounds suitabl...
Zoran Duric, Fayin Li, Harry Wechsler, Vladimir Ch...
ICCV
2005
IEEE
12 years 10 months ago
A Multi-Scale Hybrid Linear Model for Lossy Image Representation
This paper introduces a simple and efficient representation for natural images. We partition an image into blocks and treat the blocks as vectors in a high-dimensional space. We t...
Wei Hong, John Wright, Kun Huang, Yi Ma
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