[PDF] Top 20 Learning the Kernel Function via Regularization
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Learning the Kernel Function via Regularization
... We are grateful to Mark Herbster of University College London (UCL) for a remark which lead to Lemma 25, Raphael Hauser of Oxford University for suggesting a method to minimize the square loss regularization ... See full document
27
Causal Learning via Manifold Regularization
... This paper frames causal structure estimation as a machine learning task. The idea is to treat indicators of causal relationships between variables as ‘labels’ and to exploit available data on the variables of ... See full document
32
Improved Natural Language Learning via Variance Regularization Support Vector Machines
... variance regularization in particular, will increasingly be used in combination with related NLP strategies that learn better when labeled data is ...tive learning (Cohn et ... See full document
10
Locally Non Linear Learning for Statistical Machine Translation via Discretization and Structured Regularization
... 6.2 Learning Non-Linear Transformations Next, we evaluate the effects of discretization (Disc), overlapping bins ...neighbor regularization (LNR), and monotone neighbor reg- ularization (MNR) on three ... See full document
12
Regularization Techniques for Learning with Matrices
... examines regularization based on group norms and spectral norms of ...such regularization methods and provide general hints for choosing a regularization function based on the underlying ... See full document
26
A Unifying Framework in Vector-valued Reproducing Kernel Hilbert Spaces for Manifold Regularization and Co-Regularized Multi-view Learning
... unified learning framework for the case the hypothesis spaces are vector-valued ...Manifold Regularization and Co-regularized Multi-view ...general learning framework and presented the solution for ... See full document
72
Learning Theory of Distributed Regression with Bias Corrected Regularization Kernel Network
... distributed learning, however, the variance shrinks fast when m is large but the bias keeps unchanging during the average ...distributed learning. Instead, distributed learning prefers algorithms of ... See full document
25
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
... of learning algorithms based on a new form of regularization that allows us to exploit the geometry of the marginal ...graph learning algorithms and standard methods including support vector machines ... See full document
36
Learning Representations from Imperfect Time Series Data via Tensor Rank Regularization
... Tensor Methods: Tensor representations have been used for learning discriminative representa- tions in unimodal and multimodal tasks. Tensors are powerful because they can capture important higher order ... See full document
8
Heterogeneous Transfer Learning via Deep Matrix Completion with Adversarial Kernel Embedding
... transfer learning methods focus on learning problems where the source domain and target domain are represented by the same type of features (Pan et ...transfer learning ap- proach for cross-domain ... See full document
8
Multi-class Discriminant Kernel Learning via Convex Programming
... Table 6: Comparison of eight methods on the USPS data set when the first three classes are used. The eight methods, listed from top to bottom, are the SDP formulation with λ fixed as proposed in Theorem 3.1, the SDP ... See full document
40
Regularization via Mass Transportation
... supervised learning is to minimize the empirical risk, that is, the expectation of some loss function quantifying the prediction error under the empirical ...adding regularization terms to the ... See full document
68
Learning Rates of Support Vector Machine Classifiers with Data Dependent Hypothesis Spaces
... coefficient regularization was analyzed under the restriction that the kernel is positive semi-definite or has certain smoothness condition (such as Lipschitz ... See full document
6
Hyper parameter Optimisation of Gaussian Process Reinforcement Learning for Statistical Dialogue Management
... policy learning are based on reinforcement learning (RL) (Young et ...forcement learning (GPRL) the kernel function de- fines prior correlations of the objective function given ... See full document
5
Streaming kernel regression with provably adaptive mean, variance, and regularization
... noisy, function defined over a possibly large domain space. Kernel regression methods can learn such possibly non-linear functions by sharing information gathered across ...preference learning ... See full document
34
Least Square Regression Learning with Data Dependent Hypothesis and Coefficient Regularzation
... Abstract -- We study the least square regression with data dependent hypothesis and coefficient regularization algorithms based on general kernel. An explicit expression of the solution of this ... See full document
5
Sparse Kernel Regression with Coefficient-based $\ell_q-$regularization
... for learning models induced by the kernelized dictionary was ...space via approximation and concentra- tion techniques, which is a typical learning theory ...various learning models induced by ... See full document
44
Twin Support Vector Machines Based on the Mixed Kernel Function
... good learning ability, but its generalization ability is relatively ...Mixture Kernel Function (MK-TWSVM) to further improve the performance of ...global kernel function and a local ... See full document
7
Forecasting modeling with kernel function integration in gaussian processes
... a kernel within a Gaussian process for finding and learning patterns from data attributes that fit the structure of time series ...the function of the problem ...the function or kernel ... See full document
6
On Spectral Learning
... A recent trend in regularization methods in machine learning is to use matrix regularizers which are orthogonally invariant (Argyriou et al., 2007a,b; Abernethy et al., 2009; Srebro et al., 2005). In ... See full document
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