regularization term
A new regularization term based on second order total generalized variation for image denoising problems
... a regularization term and a data fidelity ...) regularization is a variational model that uses total variation as a regularization ...V regularization was first proposed by Rudin, ...
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Infinite-σ Limits For Tikhonov Regularization
... Tikhonov regularization can be used for both classification and regression tasks, but we refer to the function f as the regularized solution in all ...the regularization term, and the right-hand ...
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Robustness and Regularization of Support Vector Machines
... regularization term. In the PAC structural-risk minimization approach, regularization is chosen to minimize a bound on the generalization error based on the training error and a complexity ...
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Value Regularization and Fenchel Duality
... Recently, there has been interest in “learning the kernel”. In Section 8, we may view this as a value regularization where the kernel matrix itself is an auxiliary parameter to be optimized. The value-based ...
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Regularization destriping of remote sensing imagery
... tunable regularization parameter with a weighted regularization term to a part of the destriping functional in Bouali ...Tikhonov regularization theory, the tuning parameter allows us to prop- ...
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Word Alignment with Synonym Regularization
... We propose a novel method that incorporates synonyms from monolingual resources in a bilin- gual word alignment model. We formulate a syn- onym pair generative model with a topic variable and use this model as a ...
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Fast and Regularization less Active Contour
... T his paper proposes a method for completely eliminating re- initialization. Many methods have been proposed to overcome the re-initialization, but they require solving partial difference equation for the same. We ...
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Regularization via Mass Transportation
... • Tractability: We propose data-driven distributionally robust regression and clas- sification models that hedge against all input-output distributions in a Wasserstein ball. We demonstrate that the emerging ...
68
1 Regularization in Matrix Relevance Learning
... proposed regularization scheme prevents over-simplification, eliminates instabilities in the learning dynamics and improves the generalization ability of the considered metric adaptation ...
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A General Regularization Framework for Domain Adaptation
... as possible. This is related to the technique of Chelba and Acero (2006) where they regularize the model parameters for the target domain using the term λ || w − w s ||, where w s is the parameter vec- tor learned ...
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Learning Regularization Weight for CRF Optimization
... coherence regularization weight serves an important role of controlling the regularization strength in the CRF optimiza- tion, and has a great influence on the quality of the final ...
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Identification of source term for the ill posed Rayleigh–Stokes problem by Tikhonov regularization method
... In this paper, we study an inverse source problem for the Rayleigh–Stokes problem for a generalized second-grade fluid with a fractional derivative model. The problem is severely ill-posed in the sense of Hadamard. To ...
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RNN Architecture Learning with Sparse Regularization
... We experiment with four text classification benchmarks ( § 3), using both GloVe and BERT embeddings. As we increase the regularization strength, we end up with smaller models. These models have a better tradeoff ...
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Improving Event Detection with Dependency Regularization
... mance of the Event Detection framework, includ- ing Verb Chain Regularization, Transparent Regu- larization, and Nomlex Regularization. The exper- imental results have demonstrated the effective- ness of ...
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Regularization and Estimation in Regression with Cluster Variables
... In this paper, we propose a method that encourages cluster variables to be selected together and can incorporate available prior information on coefficient structures in variable selection. When there is no prior ...
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Sentiment Classification with Graph Co Regularization
... In this paper, we propose a novel algorithm, called graph co-regularized non-negative matrix tri- factorization (GNMTF). We construct two affinity graphs to encode the geometric information under- lying the word space ...
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Transferring Coreference Resolvers with Posterior Regularization
... posterior regularization (Ganchev et ...posterior regularization that tunes the Lagrange multipliers directly, bypassing the saddle-point problem of existing EM and alternat- ing stochastic gradient ...
11
Causal Learning via Manifold Regularization.
... Methods for learning causal structures (such as those above) are often rooted in data- generating causal models. In a quite different vein, there have been some interesting recent efforts in the direction of labelling ...
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Regularization and Iterative Methods for Monotone Variational Inequalities
... fixed-point regularization to constraint decomposition in variational inequalities,” in Proceedings of the 9th Belgian-French-German Conference on Optimization, ...
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Ergodic approximations via matrix regularization approach
... Nevertheless the Cesaro matrix is not the unique matrix with regularization properties. Toeplitz summability theorems proved that there exists a class of matrices including the Cesaro means with the same ...
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