Regularization can benefit from data pruning
Data pruning
... come from the target distribution. They showed that if the noise can be modeled as independent source then the amount of noise tolerated can be very large but strictly less than ...
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Incremental Detection Of Redundancy And Data Pruning
... human’s data from the net could effortlessly be too big to absolutely resolve with an affordable quantity of ...we can maximize the development of ER with a confined amount of work utilizing “tips,” ...
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Robust frontier estimation from noisy data: a Tikhonov regularization approach
... pxq can be characterized as the conditional right endpoint ϕ pxq “ supty ě 0 | F Y |X py|xq ă ...one can use only the observations in a local strip around x to estimate ϕ u pxq because of its local ...
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Robust frontier estimation from noisy data: a Tikhonov regularization approach
... ϕpxq can be characterized as the conditional right endpoint ϕpxq “ supty ě 0 | F Y |X py|xq ă ...one can use only the observations in a local strip around x to estimate ϕ u pxq because of its local ...
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Robust frontier estimation from noisy data: a Tikhonov regularization approach
... composed from this noise term and the one-sided inefficiency ...we can only assume an independent Gaussian ...estimator from an ill-posed ...concrete data sets from the sector of ...
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Density Based Pruning for Identification of Differentially Expressed Genes from Microarray Data
... DB pruning significantly increased the AUC values for all four popu- lar DEG algorithms especially for rank product algo- rithm with 21% increase of AUC ...DB pruning, though in different ...DB ...
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Data Pruning of Tomographic Data for the Calibration of Strain Localization Models
... experimental data, that is to say that the reduced basis used to perform this row selection comes from Equations ...the pruning method is called a model-free ...the data pruning has not ...
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Effective pruning strategies for branch and bound Bayesian networks structure learning from data
... 3. depth of τ 4. index i of the next variable V i (i ≥ d) engaged in a cycle in ϕ where ξ 2 is a ‘‘tolerance’’ threshold for selection which is data dependent. ξ 2 = 0 means we do not want to let other nodes with ...
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Local high-order regularization on data manifolds
... it can lead to degenerate functions in high-dimensional ...high-order regularization, but it has a high computational complexity and so cannot be applied to large prob- ...suffer from the degeneracy ...
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A Data-Driven Regularization Model for Stereo and Flow
... calculate data terms following Eq ( 6 ) with two different initializations: coarse-to-fine scheme of our model and StereoSLIC [ 29 ...the data-driven smoothness, otherwise the error will drop to 0 with ...
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Maximum Relative Margin and Data-Dependent Regularization
... solution can easily be perturbed by an (invertible) affine or scaling transformation of the input ...performance can be significantly ...the data to drive performance down; a syn- thetic example ...
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Learning Representations from Imperfect Time Series Data via Tensor Rank Regularization
... clean data exhibits tensors that are low- rank since high-dimensional real-world data is of- ten generated from lower dimensional latent struc- tures (Lakshmanan et ...series data exhibits ...
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Inducing Bilingual Lexica From Non Parallel Data With Earth Mover’s Distance Regularization
... induction from non-parallel ...program can stand alone to fit training data, while in our approach the EMD shows up as a regularizer in the learning ...
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Use of Tikhonov Regularization in Pressure and Rate Transient Derivative Analysis from Noisy Data
... Tikhonov Regularization Eilers (2003) introduced new method to calculate smoothed trend from experimental data in Chemistry ...for data smoothing in Chemistry ...smoothed data trend as ...
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Ensemble Pruning for Glaucoma Detection in an Unbalanced Data Set
... the data and the ensemble decision, for example obtained by majority voting, often outperforms the best single classifier ...ensemble can be explained by the average error of the single classifiers minus ...
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Efficient Data Point Pruning for One-Class SVM
... we can improve the effectiveness of applications by using knowledge from big data (Fujiwara et ...knowledge from big data (Fujiwara et ...separates data points of the target ...
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Benefit of depolarization ratio at 1064 nm for the retrieval of the aerosol microphysics from lidar measurements
... concentration can be expected from δ l,1064 data also in case of transported mineral dust, which is comparable to trans- ported volcanic ash with respect to most relevant proper- ties, like the ...
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Learning from Label Proportions with Consistency Regularization
... most data examples are unlabeled and only a few labeled data are ...pseudo-labels can misguide the model toward erroneous decision boundaries, which result in a vicious cycle of even worse ...
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Efficient and effective pruning strategies for health data de-identification
... benefits from its use of predictive properties, we performed the same exper- iments without using ...As can be seen, using predictive properties improved the execution times of the BFS strategy in every ...
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Do pelagic grazers benefit from sea ice? Insights from the Antarctic sea ice proxy IPSO 25
... Such data suggest that primary production in sea ice accounts for only small amounts of total annual production in polar waters: typically 2–10 % in the Arctic and ...levels can be sufficient for ...
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