[PDF] Top 20 Carl Gustav Jung - El Secreto de La Flor de Oro
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Distribution-Dependent Sample Complexity of Large Margin Learning
... data distribution, and show distribution-specific upper and lower bounds on the sample complexity, both governed by the margin-adapted dimen- sion of the data ...true sample ... See full document
13
Large Margin Semi-supervised Learning
... novel learning theory is developed to quantify SPSI’s generalization error as a function of complexity of the class of candidate decision functions, the sample sizes (n l ,n u ), and the ... See full document
21
The Sample Complexity of Dictionary Learning
... a distribution over signals that is known to us only through samples from ...statistical learning sense) of dictionary learning: to what extent does the performance of a dictionary chosen based on a ... See full document
50
Empirical Risk Minimization for Probabilistic Grammars: Sample Complexity and Hardness of Learning
... is distribution-free, which means that we make no assumptions about the distribution that generated the ...the distribution-free setting does not lead to the desired sample complexity ... See full document
40
Learning Factor Graphs in Polynomial Time and Sample Complexity
... mial sample complexity guarantee for the class of Markov networks of bounded ...have large tree-width ...have large connectivity ...generating distribution is not a member of the target ... See full document
9
Interpretable Preference Learning: A Game Theoretic Framework for Large Margin On-Line Feature and Rule Learning
... two-players zero-sum game, in which the considered hy- potheses spaces consist in a set of preference prototypes along with (possibly non-linear) features. Moreover, we show how feature selection naturally comes as a ... See full document
5
Distribution testing lower bounds via reductions from communication complexity
... For the sake of simplicity, throughout the overview we fix the domain Ω = [n] and fix the proximity parameter ε to be a small constant. We begin in Section 2.1 by describing a simple “vanilla” reduction for showing an ˜ ... See full document
148
Large-Sample Learning of Bayesian Networks is NP-Hard
... local distribution is similar to the positive-influence property found in the QBN literature; it specifies that the probability of a node being in its “distinguished” state necessarily increases when we change a ... See full document
5
Mr MIRA: Open Source Large Margin Structured Learning on MapReduce
... While the current demonstrated application fo- cuses on large-scale discriminative training for machine translation, the learning algorithm is gen- eral with respect to the inference algorithm em- ployed. ... See full document
239
Normality tests for dependent data: large sample and bootstrap approaches
... for dependent data have also been ...empirical distribution function (Psaradakis and V´ avra ...for dependent data is not currently available in the ... See full document
13
Spectral Learning of Latent-Variable PCFGs: Algorithms and Sample Complexity
... polynomial-time learning algorithm for an important case of hidden-variable models: hidden Markov models (Hsu et ...case, learning of HMMs is intractable ...a sample complexity that is ... See full document
19
Text Emotion Distribution Learning from Small Sample: A Meta Learning Approach
... emotion distribution evaluation, we follow existing practices (Hosseinimotlagh and Papalexakis, 2018) to simulate a small training set by randomly selecting 10% of the samples and us- ing the remaining ones for ... See full document
7
Local Identification of Overcomplete Dictionaries
... finite sample size recovery results with high probability as long as the sample size N scales as O(K 3 dS ε ˜ −2 ), where the recovery precision ˜ ε can go down to the asymptotically achievable ...has ... See full document
123
Multicategory Large-Margin Unified Machines
... Hard and soft classifiers are two important groups of techniques for classification problems. Lo- gistic regression and Support Vector Machines are typical examples of soft and hard classifiers respectively. The ... See full document
71
Perceptron like Large Margin Classifiers
... the margin β in the next round is calculated by adding to the previous one the present step otherwise β is reduced by the same ...sufficiently large value, the procedure guaran- tees that the deviation of ... See full document
8
A Gated Self attention Memory Network for Answer Selection
... deep learning based methods have been proposed to address the task (Tay et ...deep learning methods is the use of the Compare-Aggregate architecture (Wang and Jiang, ... See full document
9
Proofs of proximity for distribution testing
... and distribution testing are dissimilar: not only the tested objects are structurally different, but, just as importantly, the access to these objects is different as well (query access versus sample ... See full document
79
Perceptron Like Large Margin Classifiers
... requires margin values which become, sooner or later, smaller than the maximum margin and the effective learning rate becomes eventually small enough without decreasing faster than linearly with the ... See full document
127
Distance Metric Learning for Large Margin Nearest Neighbor Classification
... five large data sets; see Fig. 3. On the only data set with a large performance difference, 20-newsgroups, the multi-class SVMs benefited from training in the original d = 20000 dimensional input space, ... See full document
9
Large Margin Learning of Submodular Summarization Models
... For the pairwise model we use cosine similar- ity between sentences using only words in a given word group during computation. For the word coverage model we create separate features for covering words in different ... See full document
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