Multiple Kernel Learning Algorithms

By Mehmet Gönen, Ethem Alpaydin. 2011

1,988 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Statistical Learning Theory

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 Computer Vision and Pattern Recognition

In Statistical Learning Theory

Citations in the corpus

Learning kernel logistic regression in the presence of class label noise (2014, methodology): “We then propose a robust Multiple Kernel Logistic Regression algorithm (rMKLR) based on the so-called Multiple Kernel Learning (MKL) framework (an extensive survey in recent advances of MKL is given in [16]) and the Bayesian regularisation technique [9] to automate the model selection step without u”
Bayesian Learning of Kernel Embeddings (2016, methodology): “…effective and widely used method for kernel selection, and the myriad papers on multiple kernel learning (e.g. Bach et al. (2004); Sonnenburg et al. (2006); G¨onen and Alpaydın (2011)) assume that some loss function is available and thus focus on effective ways of learning combinations of kernels.”
Deep Gaussian Processes (2012, background): “…GP models have been extended to more expressive variants, for example by considering sophisticated covariance functions [Durrande et al., 2011, G¨onen and Alpaydin, 2011] or by embedding GPs in more complex probabilistic structures [Snelson et al., 2004, Wilson et al., 2012] able to learn…”
Deep Kernel Learning (2015, background): “Modern approaches attempt to learn a ﬂexible parametric family, for example, through weighted combinations of known kernels (e.g., G¨onen and Alpaydın, 2011), but are still fundamentally limited to these standard notions of distance.”
Stochastic Variational Deep Kernel Learning (2016, background): “Indeed many recent approaches have advocated building expressive kernel functions [e.g., 23, 10, 27, 26, 18, 32], and emerging research in this direction takes inspiration from deep learning models [e.g., 29, 6, 3].”
A Survey on Multi-view Learning (2013, background): “Gönen and Alpaydın (2011) have reviewed the literature on MKL.”
A Survey on Metric Learning for Feature Vectors and Structured Data (2013, background): “The interested reader may refer to the recent survey on MKL by Gönen and Alpaydin (2011).”
Gaussian Process Kernels for Pattern Discovery and Extrapolation (2013, methodology): “MKL is not typically intended for pattern discovery, and often uses mixtures of SE kernels.”
Computational Phenotype Discovery Using Unsupervised Feature Learning over Noisy, Sparse, and Irregular Clinical Data (2013, background): “…supervised feature learning, where the expert selects the learning task, a set of raw input variables and the outcome labels, and then the algorithm tries to construct features from the input variables either individually [12,15,16] or as an entire feature space [19] that best predict the outcome.”
Reborn Filters: Pruning Convolutional Neural Networks with Limited Data (2020, methodology): “This practice coheres with the idea of multiple kernel learning (MKL) (G¨onen and Alpaydın 2011; Bucak, Jin, and Jain 2014), which seeks to learn an optimal combination of pre-deﬁned kernels (aka the original ﬁlters), so that we can adequately exploit the knowledge within the already-learned ﬁlters.”
Insights from Classifying Visual Concepts with Multiple Kernel Learning (2011, background): “For an extensive treatment see the survey of [34].”
Write a Classifier: Predicting Visual Classifiers from Unstructured Text (2015, methodology): “We illustrated the value of proposing a kernelized version by applying kernels generated by Multiple Kernel Learning (MKL) and achieved better results.”
Neighborhood Regularized Logistic Matrix Factorization for Drug-Target Interaction Prediction (2016, methodology): “One direction for future work is to couple logistic matrix factorization with the multiple kernel learning techniques [44].”
Multi-modal Egocentric Activity Recognition using Audio-Visual Features (2018, methodology): “Multi-kernel learning approach (MKL) has been proposed for feature fusion using kernel-based classifiers to improve the classification performance [15].”
Gaussian Process Covariance Kernels for Pattern Discovery and Extrapolation (2013, background): “Sophisticated kernels are most often achieved by composing together a few standard kernel functions (Archambeau and Bach, 2011; Durrande et al., 2011; Gönen and Alpaydın, 2011; Rasmussen and Williams, 2006).”
Variational Auto-encoded Deep Gaussian Processes (2015, background): “These models have the ability to produce samples able to mimic the learned data and they allow principled assessment of the uncertainty in the predictions.”
Learning to Learn Kernels with Variational Random Features (2020, positive): “Pioneering works (Bach et al., 2004; Gönen & Alpaydın, 2011; Duvenaud et al., 2013) learn to combine predeﬁned kernels in a multi-kernel learning manner.”