@inproceedings{polychronou_machine_2020,
	title = {Machine {Learning} {Algorithms} for {Food} {Intelligence}: {Towards} a {Method} for {More} {Accurate} {Predictions}},
	url = {http://dx.doi.org/10.1007/978-3-030-39815-6_16},
	doi = {10.1007/978-3-030-39815-6_16},
	abstract = {It is evident that machine learning algorithms are being widely impacting
industrial applications and platforms. Beyond typical research
experimentation scenarios, there is a need for companies that wish to
enhance their online data and analytics solutions to incorporate ways in
which they can select, experiment, benchmark, parameterise and choose the
version of a machine learning algorithm that seems to be most appropriate
for their specific application context. In this paper, we describe such a
need for a big data platform that supports food data analytics and
intelligence. More specifically, we introduce Agroknow’s big data platform
and identify the need to extend it with a flexible and interactive
experimentation environment where different machine learning algorithms
can be tested using a variation of synthetic and real data. A typical
usage scenario is described, based on our need to experiment with various
machine learning algorithms to support price prediction for food products
and ingredients. The initial requirements for an experimentation
environment are also introduced.},
	publisher = {Springer International Publishing},
	author = {Polychronou, Ioanna and Katsivelis, Panagis and Papakonstantinou, Mihalis and Stoitsis, Giannis and Manouselis, Nikos},
	year = {2020},
	pages = {165--172},
}

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