A general regression technique for learning transductions

By Corinna Cortes, Mehryar Mohri, Jason Aaron Edward Weston. 2005

104 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Molecular Biology

Citations in the corpus

Kernel Mean Embedding of Distributions: A Review and Beyonds (2016, methodology): “(2009; Section 6) with connections to the solutions of Gaussian process regression (Rasmussen and Williams 2005) and kernel dependency estimation (Cortes et al. 2005). Nevertheless, Grünewälder et al. (2012) gives a more rigorous account of this perspective.”
Kernel methods in machine learning (2007, neutral): “[33]) without the need for subspace projections.”
Human3.6M: Large Scale Datasets and Predictive Methods for 3D Human Sensing in Natural Environments (2014, methodology): “mation methodology for kernel dependency estimation (KDE) [46].”
Semi-supervised Penalized Output Kernel Regression for Link Prediction (2011, unknown): “We note that this extension of OKR directly ﬁts the ﬁrst step of Kernel Dependency Estimation (KDE) reformulation of Cortes et al. (2005).”
Hilbert Space Embeddings of Hidden Markov Models (2010, background): “In some cases, however, it is possible to define the feature map ϕ(x) in such a way that an efficient algorithm for solving the optimization can be obtained, e.g. Cortes et al. (2005).”
Latent structured models for human pose estimation (2011, background): “Remarkably, the learning problem (4) can be solved in closed form [7] with parameters given by”
Hilbert space embeddings of conditional distributions with applications to dynamical systems (2009, background): “Cortes et al. (2005) studied kernel dependency estimation for transduction.”
Structured Prediction of 3D Human Pose with Deep Neural Networks (2016, methodology): “In effect, this hidden layer and the CNN-based representation of the image play the same role as the kernel embeddings in KDE-based approaches [5, 12, 14], thus allowing us to account for structure within a direct regression framework.”
A Generalized Kernel Approach to Structured Output Learning (2012, methodology): “In this problem, kPCA-based KDE performed better than the KDE formulation of Cortes et al. (2005).”
A Consistent Regularization Approach for Structured Prediction (2016, methodology): “1 and the KDE methods averaged over 10 runs.”
Input Output Kernel Regression (2016, methodology): “In this paper, we also put the reformulation of Kernel Depen-dency Estimation proposed by Cortes et al. (2005) into perspective in the Output Kernel Regression framework.”
Iterated Second-Order Label Sensitive Pooling for 3D Human Pose Estimation (2014, methodology): “The other two models are rldRF (which contains both pixel coordinates and orientation features) and rlpRF which uses the pixel location and an estimate of the 3D pose (predicted using either ridge regression (RR) or kernel dependency estimation (KDE[9, 18]) against O2LP descriptors computed based on”