Cold start (computing): Difference between revisions

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Cold start (or cold boot) may also refer to a booting process of a single [[computer]] (or [[virtual machine]]).<ref>{{Cite web|url=https://www.techopedia.com/definition/3332/cold-boot|title=What is Cold Boot? - Definition from Techopedia|website=Techopedia.com|language=en|access-date=2020-01-31}}</ref> In this case [[Windows service|services]] and other startup [[Application software|applications]] are executed after reboot. The system is typically made available to the user even though startup operations are still performed and slow down other operations.

Another type of problem is when the [[data model]] of a particular system requires connections between objects. In that case new objects will not operate normally until those connections are made. This is well known problem with [[Recommenderrecommender system|recommender systems]]s.<ref>{{cite journal |last1=Bobadilla |first1=Jesús |last2=Ortega |first2=Fernando |last3=Hernando |first3=Antonio |last4=Bernal |first4=Jesús |title=A collaborative filtering approach to mitigate the new user cold start problem |journal=Knowledge-Based Systems |date=February 2012 |volume=26 |pages=225–238 |doi=10.1016/j.knosys.2011.07.021|url=http://oa.upm.es/15302/ }}</ref><ref>{{cite journal |last1=Lika |first1=Blerina |last2=Kolomvatsos |first2=Kostas |last3=Hadjiefthymiades |first3=Stathes |title=Facing the cold start problem in recommender systems |journal=Expert Systems with Applications |date=March 2014 |volume=41 |issue=4 |pages=2065–2073 |doi=10.1016/j.eswa.2013.09.005}}</ref>

In some [[machine learning]] scenarios, with models where the training dataset is incrementally added to in time (e.g. in [[Activeactive learning (machine learning) | Activeactive learning]]), cold start refers to training the model on the so far obtained labeled pool with new data added de novo, instead of training the model on new data with all its knowledge from previous trainings (warm start). <ref>{{cite arXiv|last1=Ash |first1= Jordan |last2=Adams |first2=Ryan |title=On Warm-Starting Neural Network Training |year= 2019 |class= cs.LG |eprint=1910.08475}}</ref> Unlike the previous mentioned instances, cold starting in these scenarios can yield better results of the model.

== See also ==

* [[Cold start (recommender systems)]]

* [[{{section link|Reboot#|Cold]]}}

== References ==

<references/>

[[categoryCategory:Information systems]]