ref for interval feature

Author: qgeissmann

report/matmet.tex

@@ -63,7 +63,7 @@ \subsection{Feature extraction from time series}
 
 
 \subsection{Addition of temporal features}
-Two strategies, ``lag'' and ``convolution'', were used to add temporal
+Two strategies, ``lag'' and ``convolution''(\ie{} a case of interval feature\cite{rodriguez_support_2005}), were used to add temporal
 information to the variable space\cite{dietterich_machine_2002}.
 For lag, given a vector $\mathbf{X_t}$ of feature at time $t$,
 A new vector of feature was defined as $\mathbf{{X'}_t}$ as

report/report.bib

@@ -1346,4 +1346,25 @@ @article{diekelmann_memory_2010
 	year = {2010},
 	pages = {114--126},
 	file = {Full Text PDF:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/6S2UIPTX/Diekelmann and Born - 2010 - The memory function of sleep.pdf:application/pdf;Snapshot:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/FVMUBM98/nrn2762.html:text/html}
+}
+
+@article{rodriguez_support_2005,
+	series = {{AI}-2004, Cambridge, England, 13th-15th December 2004},
+	title = {Support vector machines of interval-based features for time series classification},
+	volume = {18},
+	issn = {0950-7051},
+	url = {http://www.sciencedirect.com/science/article/pii/S0950705105000432},
+	doi = {10.1016/j.knosys.2004.10.007},
+	abstract = {In previous works, a time series classification system has been presented. It is based on boosting very simple classifiers, formed only by one literal. The used literals are based on temporal intervals.
+
+The obtained classifiers were simply a linear combination of literals, so it is natural to expect some improvements in the results if those literals were combined in more complex ways. In this work we explore the possibility of using the literals selected by the boosting algorithm as new features, and then using a {SVM} with these metafeatures. The experimental results show the validity of the proposed method.},
+	number = {4–5},
+	urldate = {2014-09-08},
+	journal = {Knowledge-Based Systems},
+	author = {Rodríguez, Juan José and Alonso, Carlos J. and Maestro, José A.},
+	month = aug,
+	year = {2005},
+	keywords = {Boosting, Kernel methods, Time series classification},
+	pages = {171--178},
+	file = {ScienceDirect Full Text PDF:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/I9EZDS2J/Rodríguez et al. - 2005 - Support vector machines of interval-based features.pdf:application/pdf;ScienceDirect Snapshot:/home/quentin/.mozilla/firefox/kkgy4t0w.default/zotero/storage/4B4QII39/S0950705105000432.html:text/html}
 }