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Kernel methods for vector output

Kernel methods are a well-established tool to analyze the relationship between input data and the corresponding output of a function. Kernels… Expand
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Related topics

Related topics

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Bayesian interpretation of kernel regularizationCross-validation (statistics)Gaussian processGraph kernel
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Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
Highly Cited
2016
Highly Cited
2016
Multi-target regression via input space expansion: treating targets as inputs
In many practical applications of supervised learning the task involves the prediction of multiple target variables from a common… Expand
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2016
2016
Multiple Output Regression with Latent Noise
In high-dimensional data, structured noise caused by observed and unobserved factors affecting multiple target variables… Expand
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2015
2015
Multi-target regression from high-speed data streams with adaptive model rules
  • J. Duarte, João Gama
  • IEEE International Conference on Data Science and…
  • 2015
    • Corpus ID: 18262582
Many real life prediction problems involve predicting a structured output. Multi-target regression is an instance of structured… Expand
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2013
2013
Habitat modeling with single- and multi-target trees and ensembles
Habitat modeling studies the influence of abiotic factors on the abundance of a given taxonomic group of organisms. In this work… Expand
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Highly Cited
2012
Highly Cited
2012
Multi-Label Classification Methods for Multi-Target Regression
Real world prediction problems often involve the simultaneous prediction of multiple target variables using the same set of… Expand
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Highly Cited
2011
Highly Cited
2011
Multi-label Problem Transformation Methods: a Case Study
Traditional classication algorithms consider learning problems that contain only one label, i.e., each example is associated with… Expand
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2010
2010
The Emotional Machine: A Machine Learning Approach to Online Prediction of User's Emotion and Intensity
This paper explores the feasibility of equipping computers with the ability to predict, in a context of a human computer… Expand
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2008
2008
Empirical Asymmetric Selective Transfer in Multi-objective Decision Trees
We consider learning tasks where multiple target variables need to be predicted. Two approaches have been used in this setting… Expand
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2002
2002
Ranking with Predictive Clustering Trees
A novel class of applications of predictive clustering trees is addressed, namely ranking. Predictive clustering trees, as… Expand
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2002
2002
Relational ranking with predictive clustering trees
A novel class of applications of predictive clustering trees is addressed, namely relational ranking. Predictive clustering trees… Expand
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