data selection
A. Data Selection
... Abstract—The issue of modelling and forecasting the share prices of the banking sector remains a challenge because of high volatilities in individual stock prices. Reliably forecasting the future values of shares is ...
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Data Selection With Fewer Words
... While shown to be effective, however, word- based scores may not capture all facets of rele- vance. The strategy of a hybrid word/POS rep- resentation was first explored by Bulyko et al. (2003), who used class-dependent ...
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A Case Study in Experimental Exploration: Exploratory Data Selection at the Large Hadron Collider
... the data selection procedure carried out in the ATLAS experiment illustrates an exploratory procedure in the sense suggested by the present ...particular data selection procedure is ...
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Dynamic Data Selection for Neural Machine Translation
... in data selection experiments, which is likely caused by the lack of in-domain data in the general ...dynamic data selection for NMT, we test whether bitext ranking for NMT can be ...
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Dynamically Composing Domain Data Selection with Clean Data Selection by “Co Curricular Learning” for Neural Machine Translation
... of data quality for neural machine ...domain- data selection, clean-data selection, or their static combination, leaving the dynamic in- teraction across them not explicitly ...
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Submodularity for Data Selection in Machine Translation
... MT data selection (Section 3) and analyze them with respect to their submodular ...SMT data selection objective and present a new class of submodular functions tailored towards this ...the ...
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Data source Data Selection Data Cleaning
... e Data mining is concerned with the process of computationally extracting hidden knowledge structures represented in models and patterns from large data ...in data. According to this definition, ...
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Data Selection for IT Texts using Paragraph Vector
... In this section the submitted system U HDS doc2vec is described. The filtering procedure receives as input the bilingual in- domain corpus I n, the bilingual general domain G en, the number of most similar sentences N ...
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Data selection in EEG signals classification
... According to our experiment, the proposed GE difference based channel selection method achieves as high as 91.67% classification accuracy by using only 19 out of 64 channels of data for [r] ...
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Learning Discourse Relations with Active Data Selection
... Since the committee- based sampling method was originally devel- oped for probabilistic classifiers, we extended the method for a decision tree classifier, us-.. ing a statistical techni[r] ...
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DataSlicer: Enabling Data Selection for Visual Data Exploration
... for data exploration and ...which data slices could be useful to human viewers when ...Typically, data slices in these and other projects are suggested to the users based on generic expectations ...
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On improving the selection of Thellier-type paleointensity data
... paleointensity data selection, we require an extensive data set from speci- mens where the expected paleointensity is ...raw data from 395 speci- mens obtained from historical volcanoes or ...
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A State of Art Analysis of Telecommunication Data by k Means and k Medoids Clustering Algorithms
... of data for the business and real time ...discovery. Data are normally preprocessed through data cleaning, data integration, data selection, and data transformation and ...
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Feature Selection Techniques and Microarray Data: A Survey
... feature selection algorithms are designed to handle learning tasks with single knowledge supply, though the capability of mistreatment auxiliary knowledge sources in multi- source feature choice might greatly ...
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A COMBINED ALGORITHM FOR DATA WAREHOUSE FRAGMENTATION SELECTION
... The data warehouse schema consisting of set of fact and dimension tables along with their attributes is ...the data warehouse administrator could manage only limited set of partitions or fragments in the ...
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Feature Selection for Efficient Economic Data Analytics
... Task 1 : Feature Selection. Let A = a1, a2, ..., am be a corpus of m economic indicators. Among these m indicators, there are m features more relevant to economic development than others. And they can be grouped ...
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A Comparative Study on Data Perturbation with Feature Selection
... feature selection before data perturbation is a better ap- proach than the other way around for classification purpose, since there is no major distinguishable contrasts in predict outcomes and discarded ...
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Wavelet basis selection for spectroscopic data analysis
... spectral data has lead to increased predictability and model simplification in regression applications when compared to traditional pre-processing techniques like PCA or ...wavelet selection for pre- ...
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Model selection with overdispersed distance sampling data
... estimating animal density as the product of group density and mean group size (Buckland et al. 2001). However, this is only effective if the size and central location of the group are measured accurately (Buckland et al. ...
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Benchmarking attribute selection techniques for microarray data
... Feature selection helps to improve prediction quality, reduce the computation time, complexity of the model and build models that are easily ...Feature selection removes the irrelevant and redundant ...
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