Feature selection and model selection for supervised learning algorithms

... concerned about feature selection and model selection in supervised learning Specifically, three feature selection methods and one model selection method are proposed The first feature selection method ... between supervised learning and unsupervised learning, in which a few labeled and a large amount of unlabeled data are available Hence, semi -supervised...

Ngày tải lên: 10/09/2015, 15:47

... a language model (LM) and Na¨ve Bayes (NB); and a “counting” method ı SumLM (Bergsma et al., 2009) Each model produces a score for a candidate in the confusion set Since all of the models are ... the correction candidates Given a preposition s in text, the prior for candidate p is the probability that p is the correct preposition for s If a model is trained on native data wi...

Ngày tải lên: 23/03/2014, 16:20

... constructs condensed feature sets through discrete feature potency estimation over unsupervised data We demonstrated that COFER based on our feature potency estimation can offer informative dense ... original feature, and some original features from not being mapped to any condensed feature Namely, ∪m ∩ Sm = ∅ for all m S and m , where m = m , and M Sm ⊆ F hold m=1 The...

Ngày tải lên: 07/03/2014, 22:20

... select features, is quite natural We note, however, that kernels are calculated using the DP algorithm Therefore, it is not clear how to calculate kernels efficiently with a statistical feature selection ... 2003 Word-Sequence Kernels Journal of Machine Learning Research, 3:1059–1082 M Collins and N Duffy 2001 Convolution Kernels for Natural Language In Proc of Neural Inf...

Ngày tải lên: 23/03/2014, 19:20

... significantly better performance on the whole Gabor feature set (Gabor- GDA) than LDA (Gabor- LDA), we also performed LDA on the selected informative Gabor features (InfoGabor-LDA) for comparison The ... perform GDA on the selected Gabor features (InfoGabor-GDA) for face recognition To show the robustness and efficiency of the proposed methods, we also perform GDA on the whole Gab...

Ngày tải lên: 22/06/2014, 23:20

... word and assigns word senses to new test instances of the same word, (supervised) metonymy recognition can be trained on a set of labelled instances of different words of one semantic class and ... 1997; Stern, 1931)) In a place -for- people pattern, a place stands for any persons/organisations associated with it, e.g., for sports teams in (2), (3), and (4), and for the g...

Ngày tải lên: 08/03/2014, 04:22

Ngày tải lên: 30/03/2014, 17:20

Ngày tải lên: 24/11/2014, 02:03

... Review of Detonation Physics 1.2.2 Numerical simulation of reacting flows 1.2.3 Numerical simulation of detonation waves 1.2.4 Model order reduction for reacting ... of reduction in the computational time (about 5.0 for (1) and 10.0 for (2)) Monte-Carlo simulations are performed for the reduced model to estimate variability in the outputs of intere...

Ngày tải lên: 10/09/2015, 15:48

... models for semi- supervised learning 12 1.4 Discriminative models for semi- supervised learning 15 1.5 Graph based semi- supervised learning 22 1.5.1 Graph based semi- supervised learning ... 2005) as an example Graph based semi- supervised learning In this section, we review the graph based semi- supervised learning algorithms These al- gor...

Ngày tải lên: 22/10/2015, 21:18

... Joint Feature Selection in Distributed Stochastic Learning The following discussion of learning methods is based on pairwise ranking in a Stochastic Gradient Descent (SGD) framework The resulting ... from scaling discriminative training for SMT to large training sets We deploy generic features for SCFG-based SMT that can efficiently be read off from rules at runtime...

Ngày tải lên: 19/02/2014, 19:20

... ingredients: feature selection and order identification Feature selection was formalized as a constrained optimization problem, the output of which was a set of important features to determine word senses ... counting second order co-occurrence was 50 words 3.2 Evaluation method for feature selection For evaluation of feature selection, we used mutual information bet...

Ngày tải lên: 20/02/2014, 16:20

... need the bits to code the index of the feature (i.e., which feature from amongst the total m candidate features) and the bits to code the coefficient of this feature The total cost can be represented ... feature selection algorithm, we code lf by using log(m) bits (where m is the total number of candidate features), which is equivalent to the standard RIC (or the Bonferroni penalty...

Ngày tải lên: 17/03/2014, 02:20

Ngày tải lên: 13/11/2014, 10:05