jbmere
pn2011
Recently Published
BN_03
Third trial for the targeted BN
BN_QC
First approach to a 2L BN
Estudio de residuos
Test de Kolmogorov - Smirnov para influencia de delta Logg y delta ZETA
Residuos PHOENIX vs CARMENES
Estudio del residuo cuando Carmenes se compara con Phoenix (tanto el teórico como +- 0.1 dex desviados en Log(G)
A-DIF Feature Creation
Data extraction and feature creation
Temperature compared between the predicted by the models and Rojas-Ayala.
Temperature compared between the predicted by the models and Rojas-Ayala.
Met_compara_Gaidos_IPAC
Met_compara_Gaidos_IPAC
CO2-Energy-GDP
Preliminary study about Mr Quo's data sets
LLP_v2
Learning patterns CPDnA
prep_GA_IPACrv_NT11F2_v2
Prediction of T para IPAC with features from GA. Comparison of T against new values of T provided by LSB
prep_GA_IPACrv_NM11F2_v1
Met estimated for IPAC with compensated doppler effects.
prep_GA_IPACrv_NG11F2_v1
G estimations for IPAC dataset with GA
prep_GA_IPACrv_NT11F2_v1
Predicción de T en los IPAC con modelos derivados de los GA pero los IPAC tienen el doppler corregido.
prep_GA_IPAC_NG11F2_v1
IPAC M_stars Log (G) predicted by ML Mid of June 2015
GA_T_IPAC_NT11F2_10
GA based T forecast for IPAC with 10 pixel bandwidth
prep_GA_IPAC_NT11F2-30_v1
Simulación con GA para segmentos de banda de 30 pixels
prep_GA_case01_NG11F2_v1
Log(G) estimations based on different predicting technologies
prev_01
Análisis preliminar del modelo proporcionado por S Capuz
features_01
Feature identification for signal segmentation
H4_01
Internal work
prep_2014
Data Treatment for BL's samples
Process of video components. VideoComparison Release 01
Comparison between videos
Training Models From GA selected features
It is based on predicting T trained from 5-features from BT-SETTL
Training Models From GA selected features
Training Models From GA selected features
Target is T in BT-SETTL for 4 features
Training Models From GA selected features NL SVM for Metalicity Prediction
Training Models From GA selected features for Metalicity Prediction
Non Linear. Suppor Vector Machine
Training Models From GA selected features NL RandomForest for Gravity Prediction
Training Models From GA selected features for Gravity Prediction.
Nonlinear GA by using RF
HFT_01
Initial steps in HFT by considering technical indexes without memory and short term prediction
Processing data report for JEF station
Initial analysis of data
Ongoing work about engine oil degradation
Work being done
Stil preliminary results
M_prep_rep_01v_01
Preliminar analysis of Class M stars
Preprocessing data for used oil viscosities (40º and 100ºC) and Total Acid Number (TAN)
First steps on the oils analyses
Experimental SNR determination from ELODIE data-set.
Sample report about an ongoing research related to SNR in stellar spectra