Feature extraction and classification on Time Series
Visualitza/Obre
Estadístiques de LA Referencia / Recolecta
Inclou dades d'ús des de 2022
Cita com:
hdl:2117/109803
Realitzat a/ambPurdue University
Tipus de documentProjecte Final de Màster Oficial
Data2017-06-29
Condicions d'accésAccés obert
Tots els drets reservats. Aquesta obra està protegida pels drets de propietat intel·lectual i
industrial corresponents. Sense perjudici de les exempcions legals existents, queda prohibida la seva
reproducció, distribució, comunicació pública o transformació sense l'autorització del titular dels drets
Abstract
Time is involved in almost every scienti c eld one can think on.
Observations of a phenomena are collected with the aim of study or
explain its behavior. This collections lead to organized data called time
series.
Data mining community has spent a reasonable amount of time
studying time series, in order to extract all meaningful knowledge from
them. Humans are generally good comparing time series, but still, our
capabilities are not scalable and we need to design algorithms and
techniques that allow us to deal with high dimensional data and other
problems.
In this work we will focus in a speci c problem, extracting valid
features of unlabeled time series obtained from aircraft sensors. These
must serve as a summary of a ight and they also must include relevant
details that serve to characterize it. This information will be used to
feed an algorithm which can learn to classify ights in groups, reducing
the number of necessary labeled data to obtain the desired accuracy
using an active learning approach.
TitulacióMÀSTER UNIVERSITARI EN INTEL·LIGÈNCIA ARTIFICIAL (Pla 2017)
Col·leccions
Fitxers | Descripció | Mida | Format | Visualitza |
---|---|---|---|---|
122080.pdf | 1,505Mb | Visualitza/Obre |