@conference {Banko2007Open,
	title = {Open Information Extraction from the Web},
	booktitle = {Proceedings of the 20th International Joint Conference on Artificial Intelligence},
	year = {2007},
	pages = {2670{\textendash}2676},
	publisher = {IJCAI},
	organization = {IJCAI},
	address = {Hyderabad, India},
	keywords = {datamining, it, textmining},
	url = {http://dli.iiit.ac.in/ijcai/IJCAI-2007/PDF/IJCAI07-429.pdf},
	author = {Banko, Michele and Cafarella, Michael J. and Soderland, Stephen and Broadhead, Matthew and Etzioni, Oren},
	editor = {Veloso, Manuela M.}
}
@article {Atkinson2007Exploratory,
	title = {Exploratory tools for clustering multivariate data},
	journal = {Computational Statistics \& Data Analysis},
	volume = {52},
	number = {1},
	year = {2007},
	month = {September},
	pages = {272{\textendash}285},
	abstract = {The forward search provides a series of robust parameter estimates based on increasing numbers of observations. The resulting series of robust Mahalanobis distances is used to cluster multivariate normal data. The method depends on envelopes of the distribution of the test statistics in forward plots. These envelopes can be found by simulation; flexible polynomial approximations to the envelopes are given. New graphical tools provide methods not only of detecting clusters but also of determining their membership. Comparisons are made with mclust and k -means clustering.},
	keywords = {algorithms, cluster, data-exploration, it},
	issn = {01679473},
	doi = {10.1016/j.csda.2006.12.034},
	url = {http://dx.doi.org/10.1016/j.csda.2006.12.034},
	author = {Atkinson, A. and Riani, M.}
}
@article {Mazarakis2007Vehicle,
	title = {Vehicle classification in Sensor Networks using time-domain signal processing and Neural Networks},
	journal = {Microprocessors and Microsystems},
	volume = {31},
	number = {6},
	year = {2007},
	month = {September},
	pages = {381{\textendash}392},
	abstract = {Vehicle classification is a demanding application of Wireless Sensor Networks. In many cases, sensor nodes detect and classify vehicles from their acoustic and/or seismic signature using spectral or wavelet based feature extraction methods. Such methods, while providing good results are quite demanding in computational power and energy and are difficult to implement on low-cost sensor nodes with limited resources. In this work, we investigate the use of a time-domain encoding and feature extraction method, to produce simple, fixed-size matrices from complex acoustic and seismic signatures of vehicles for classification purposes. Classification is accomplished using an Artificial Neural Network and a basic, L1 distance, archetype classifier. Hardware implementation issues on a prototype sensor node, based on an 8-bit microcontroller, are also discussed. For evaluation purposes we use real data from DARPA{\textquoteright}s SensIt project, which contains various acoustic and seismic signatures from two different vehicle types, a tracked vehicle and a heavy truck.},
	keywords = {asr, classification, it, neuralnetworks},
	issn = {01419331},
	doi = {10.1016/j.micpro.2007.02.005},
	url = {http://dx.doi.org/10.1016/j.micpro.2007.02.005},
	author = {Mazarakis, G. and Avaritsiotis, J.}
}
