[PDF] Top 20 Instance Selection for Machine Translation using Feature Decay Algorithms
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Instance Selection for Machine Translation using Feature Decay Algorithms
... A feature decay algorithm (FDA) aims to max- imize the coverage of the target language features (such as words, bigrams, and phrases) for the test ...language feature that does not ap- pear in the ... See full document
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Feature Decay Algorithms for Fast Deployment of Accurate Statistical Machine Translation Systems
... use feature decay algorithms (FDA) for fast deployment of accurate statistical machine translation systems taking only about half a day for each translation direc- ...the ... See full document
7
ParFDA for Instance Selection for Statistical Machine Translation
... of feature decay algorithms (FDA), a class of instance selection algorithms that use fea- ture decay, developed for fast deployment of accu- rate SMT ... See full document
7
A survey on feature selection to perform classification using Meta Heuristic algorithms in Data Mining Domain
... are instance- based or lazy learners in that they store all of the training samples and do not build a classifier until a new (unlabeled) sample needs to be ...all machine learning ... See full document
12
Priorities Of Developers Based On Instance Selection and Feature Selection Technique
... system using machine learning algorithms ,which is classification of text bugs from the sets of bug ...bug using feature selection ...of using instance ... See full document
6
Comparison of Feature selection Methods and Algorithms
... Abstract: Feature selection is an main topic in data mining, particularly for high dimensional ...datasets. Feature selection (also known as subset selection) is a method normally used ... See full document
10
The Diagnosis of Epilepsy by Gravitational Search Algorithm and Support Vector Machines
... by using wavelet transform and fast fractional Fourier ...perform feature selection, instance selection and parameters optimization of support vector machines, and finally constructed ... See full document
15
A Bayesian non-linear method for feature selection in machine translation quality estimation
... where x ∗ is a test input and y ∗ is its response value. The posterior p(f |D) reflects our updated belief over possible functions after observing the training set D, i.e., f should pass close to the response values for ... See full document
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Can Machine Learning Algorithms Improve Phrase Selection in Hybrid Machine Translation?
... hybrid machine translation (MT) system that has been extended with a machine learning component controlling its phrase ...more translation engines which could be substituted into our ... See full document
6
Machine Translation with parfda , Moses, kenlm nplm , and PRO
... Parallel feature weight decay algorithms (parfda) (Bic¸ici, 2018) is an instance se- lection tool we use to select training and language model instances to build Moses (Koehn et ... See full document
7
Instance Weighting for Neural Machine Translation Domain Adaptation
... Neural Machine Translation (NMT) domain adaptation, the sentence selection can also be used (Chen et ...trained using out-of-domain data, and then further trained using in-domain ... See full document
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Genetic variants and their interactions in disease risk prediction – machine learning and network perspectives
... Although machine learning methods are better targeted at individual-level prediction making, the feature selection methods would also benefit from more stratified options, for instance, in ... See full document
16
Context Adaptation in Statistical Machine Translation Using Models with Exponentially Decaying Cache
... on translation quality. Using our hy- pothesis of translation consistency we expected another gain on our test ...cache translation op- tions for which the transition costs (of adding this ... See full document
8
Discriminative Instance Weighting for Domain Adaptation in Statistical Machine Translation
... There is a fairly large body of work on SMT adaptation. We introduce several new ideas. First, we aim to explicitly characterize examples from OUT as belonging to general language or not. Pre- vious approaches have tried ... See full document
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Title: A Technique of Combine Feature Selection and Instance Selection for Effective Bug Triage
... Step 5: Calculate zero count for each word in all reports. Step 6: Remove all words having information gain less than T. Where T is user define threshold value. Step 7: Calculate performance of Feature ... See full document
6
Lost in Translation: Loss and Decay of Linguistic Richness in Machine Translation
... In the field of MT, the concept of lexical loss/diversity and its importance is indirectly re- lated to the research of Wong and Kit (2012) on cohesion. They illustrated the relevance of the under-use of linguistic ... See full document
11
A Survey on Classification Techniques in Internet Environment Akarshika Rawat, Ankita Choubey
... Feature selection and sample ...four feature selection techniques, namely chi-square from statistics, frequent pattern mining and clustering from data mining, and community detection from ... See full document
8
A Semantic Feature for Statistical Machine Translation
... tional feature in the log-linear combination (Och & Ney, 2002) of models of a phrase-based translation ...a feature, which is dynamic in the sense that depends on the input sentence to be ... See full document
9
A Divisive Information Theoretic Feature Clustering Algorithm for Text Classification (Kernel Machines Section)
... single feature and thus dimensionality can be drastically ...such feature clustering is more effective than feature selection(Yang and Pedersen, 1997), especially at lower number of ...the ... See full document
23
Learning Non linear Features for Machine Translation Using Gradient Boosting Machines
... ping point and other hyperparameters of the boost- ing method, and a Test set for reporting final re- sults. For Chinese-English, the training corpus consists of approximately one million sentence pairs from the FBIS and ... See full document
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