A Machine Learning Based Approach for Vocabulary Selection for Speech Transcription

2013 ◽  
pp. 60-67 ◽  
Author(s):  
Denis Jouvet ◽  
David Langlois
Keyword(s):  
Machine Learning ◽  
Selection For ◽  
Speech Transcription

Feature Selection for Unsupervised Machine Learning of Accelerometer Data Physical Activity Clusters – A Systematic Review

2021 ◽  
Author(s):  
Petra J. Jones ◽  
Mike Catt ◽  
Melanie J. Davies ◽  
Charlotte L. Edwardson ◽  
Evgeny M. Mirkes ◽  
...  
Keyword(s):  
Physical Activity ◽  
Machine Learning ◽  
Systematic Review ◽  
Feature Selection ◽  
Accelerometer Data ◽  
Unsupervised Machine Learning ◽  
Selection For

scholarly journals Application of all relevant feature selection for failure analysis of parameter-induced simulation crashes in climate models

2015 ◽  
Vol 8 (7) ◽  
pp. 5419-5435 ◽  
Author(s):  
W. Paja ◽  
M. Wrzesień ◽  
R. Niemiec ◽  
W. R. Rudnicki
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Climate Models ◽  
Original Study ◽  
Relative Importance ◽  
Relevant Feature ◽  
Machine Learning Methods ◽  
Selection For ◽  
Robust Prediction ◽  
Physical Components

Abstract. The climate models are extremely complex pieces of software. They reflect best knowledge on physical components of the climate, nevertheless, they contain several parameters, which are too weakly constrained by observations, and can potentially lead to a crash of simulation. Recently a study by Lucas et al. (2013) has shown that machine learning methods can be used for predicting which combinations of parameters can lead to crash of simulation, and hence which processes described by these parameters need refined analyses. In the current study we reanalyse the dataset used in this research using different methodology. We confirm the main conclusion of the original study concerning suitability of machine learning for prediction of crashes. We show, that only three of the eight parameters indicated in the original study as relevant for prediction of the crash are indeed strongly relevant, three other are relevant but redundant, and two are not relevant at all. We also show that the variance due to split of data between training and validation sets has large influence both on accuracy of predictions and relative importance of variables, hence only cross-validated approach can deliver robust prediction of performance and relevance of variables.

scholarly journals INSTANCE – the Italian seismic dataset for machine learning

2021 ◽  
Vol 13 (12) ◽  
pp. 5509-5544
Author(s):  
Alberto Michelini ◽  
Spina Cianetti ◽  
Sonja Gaviano ◽  
Carlo Giunchi ◽  
Dario Jozinović ◽  
...  
Keyword(s):  
Machine Learning ◽  
Transfer Functions ◽  
Strong Motion ◽  
Network Code ◽  
Waveform Data ◽  
Magnitude Range ◽  
Comprehensive Information ◽  
Selection For ◽  
Learning Analysis ◽  
Italian Earthquake

Abstract. The Italian earthquake waveform data are collected here in a dataset suited for machine learning analysis (ML) applications. The dataset consists of nearly 1.2 million three-component (3C) waveform traces from about 50 000 earthquakes and more than 130 000 noise 3C waveform traces, for a total of about 43 000 h of data and an average of 21 3C traces provided per event. The earthquake list is based on the Italian Seismic Bulletin (http://terremoti.ingv.it/bsi, last access: 15 February 2020​​​​​​​) of the Istituto Nazionale di Geofisica e Vulcanologia between January 2005 and January 2020, and it includes events in the magnitude range between 0.0 and 6.5. The waveform data have been recorded primarily by the Italian National Seismic Network (network code IV) and include both weak- (HH, EH channels) and strong-motion (HN channels) recordings. All the waveform traces have a length of 120 s, are sampled at 100 Hz, and are provided both in counts and ground motion physical units after deconvolution of the instrument transfer functions. The waveform dataset is accompanied by metadata consisting of more than 100 parameters providing comprehensive information on the earthquake source, the recording stations, the trace features, and other derived quantities. This rich set of metadata allows the users to target the data selection for their own purposes. Much of these metadata can be used as labels in ML analysis or for other studies. The dataset, assembled in HDF5 format, is available at http://doi.org/10.13127/instance (Michelini et al., 2021).

scholarly journals You Are the Only Possible Oracle: Effective Test Selection for End Users of Interactive Machine Learning Systems

2014 ◽  
Vol 40 (3) ◽  
pp. 307-323 ◽  
Author(s):  
Alex Groce ◽  
Todd Kulesza ◽  
Chaoqiang Zhang ◽  
Shalini Shamasunder ◽  
Margaret Burnett ◽  
...  
Keyword(s):  
Machine Learning ◽  
End Users ◽  
Learning Systems ◽  
Test Selection ◽  
Interactive Machine Learning ◽  
Selection For
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