[PDF] Top 20 On the generalization of soft margin algorithms
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On the generalization of soft margin algorithms
... a soft margin boosting algorithm ...hard margin penalty to individual margin errors and hence can suffer from overfitting if the training data is noisy and difficult to separate with the ... See full document
15
Wasserstein Soft Label Propagation on Hypergraphs: Algorithm and Generalization Error Bounds
... of soft la- bel ...our generalization bound is the first of its type in the literature of Wasserstein distance based soft label propaga- tion; on graphs these results generalize the ... See full document
8
Dynamics and Generalization Ability of LVQ Algorithms
... Learning vector quantization (LVQ) schemes constitute intuitive, powerful classification heuris- tics with numerous successful applications but, so far, limited theoretical background. We study LVQ rigorously within a ... See full document
38
Less but Better: Generalization Enhancement of Ordinal Embedding via Distributional Margin
... embedding algorithms always need a large number of labeled data to predict unknown similar- ity relationship among items from learned embedded ...task, generalization ability is the main chal- lenge when we ... See full document
8
Title: An Endowed Takagi-Sugeno-type Fuzzy Model for Classification Problems
... the soft margin helps to reduce the bound on the generalization error in the feature space and helps to improve the fuzzy model generalization ... See full document
6
Generalization Bounds for Ranking Algorithms via Algorithmic Stability
... of generalization properties of ranking algorithms has so far been investigated mainly in the special setting of bipartite ...of generalization properties in more general ...a margin-based ... See full document
34
Maximum Margin Algorithms with Boolean Kernels
... the margin (or the dimension required) for concrete concept ...and margin are for all the examples (or a large fraction of the instance space) and not just for a small ... See full document
25
Support Vector Machine Soft Margin Classifiers: Error Analysis
... error or stability analysis (e.g. Vapnik, 1998; Bousquet and Ellisseeff, 2002; Zhang, 2004), and some other advanced empirical process techniques such as the local Rademacher averages (van der Vaart and Wellner, 1996; ... See full document
33
Soft Margin Support Vector Classification as Buffered Probability Minimization
... C-SVM, soft-margin support vector classifier is equivalent to minimization of Buffered Probability of Exceedance (bPOE), a recently intro- duced characterization of ...connect soft margin ... See full document
43
Approximate Maximum Margin Algorithms with Rules Controlled by the Number of Mistakes
... hard margin optimisation task in the extended space is equivalent to the soft margin optimisation in the original space if the 2-norm of the slack vari- ables is employed ...this soft ... See full document
11
Robust Bounds on Generalization from the Margin Distribution
... "Soft Margin", which will be described ...his Soft Margin Classifer is similar, albeit with totally dierent motivations: in order to minimize the training error of the output hy- ... See full document
28
A Refined Margin Analysis for Boosting Algorithms via Equilibrium Margin
... the margin theory. We propose a new upper bound for the generalization error of voting ...minimum margin bound. The key factor in this bound is a new margin notion which we refer to as the ... See full document
29
Algorithms for Real Time Rendering of Soft Shadows
... Shadow mapping has a number of useful advantages that have helped it become one of the more popular shadow algorithms in common use. As well as being imple- mentable on common graphics hardware, its image-based ... See full document
57
Exponentiated Gradient Algorithms for Conditional Random Fields and Max-Margin Markov Networks
... (EG) algorithms for training such models, where EG updates are applied to the convex dual of either the log-linear or max- margin objective function; the dual in both the log-linear and max-margin ... See full document
48
Analysis of the Marketing Margin of Soyabeans in Benue State, Nigeria
... Data for this study was collected from primary sources. The primary data was generated by the researcher through the use of trained enumerators, personal interview techniques and a well structured questionnaires which ... See full document
7
Quantitative Study of the Effectiveness of Soft Laser on the Activity of the Blood Vessels in the Spleen of Diseased Mice with Cancer
... of soft laser in increasing the number of blood vessels of the spleen and then counting them in both normal and diseased mice with cancer in order to study the mentioned results ... See full document
5
The view from 10,000 procedures: technical tips and wisdom from master pancreatic surgeons to avoid hemorrhage during pancreaticoduodenectomy
... Pancreaticoduodenectomy remains the exclusive technique for surgical resection of cancers located within both the pancreatic head and periampullary region. Amongst peri-procedural complications, hemorrhage is ... See full document
5
Enhanced LMNN Using Ball trees in CBIR
... of algorithms and methods are there which is based on distance vectors to retrieve an ...recent algorithms is called as large margin nearest neighbor algorithms in the area of machine ... See full document
16
The Effect of Different Regression-Based Algorithms on Frequency Based EMG Fatigue
... is a known method to estimate the development of NMF, but using the D-max regression based algorithm associated with EMG frequency has been untested. Our hypothesis was that the two methods were not significantly ... See full document
9
Dual Coordinate Descent Algorithms for Efficient Large Margin Structured Prediction
... Algorithms for training SSVM Cutting plane (CP) methods (Tsochantaridis et al., 2004; Joachims et al., 2009) have been the dominant method for learning the L1-Loss SSVM. Eq. (2) contains an exponential number of ... See full document
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