Lingua: Inglese
Editore: CreateSpace Independent Publishing Platform, 2017
ISBN 10: 1978304870 ISBN 13: 9781978304871
Da: Gate City Books, GREENSBORO, NC, U.S.A.
Condizione: good. USED book in GOOD condition. Great binding, pages and cover show normal signs of wear from use.
Lingua: Inglese
Editore: CreateSpace Independent Publishing Platform, 2017
ISBN 10: 1978304870 ISBN 13: 9781978304871
Da: GreatBookPrices, Columbia, MD, U.S.A.
EUR 27,17
Quantità: Più di 20 disponibili
Aggiungi al carrelloCondizione: As New. Unread book in perfect condition.
Lingua: Inglese
Editore: CreateSpace Independent Publishing Platform, 2017
ISBN 10: 1978304870 ISBN 13: 9781978304871
Da: GreatBookPrices, Columbia, MD, U.S.A.
EUR 46,80
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Aggiungi al carrelloCondizione: New.
Lingua: Inglese
Editore: CreateSpace Independent Publishing Platform, 2017
ISBN 10: 1978304870 ISBN 13: 9781978304871
Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 32,20
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Aggiungi al carrelloCondizione: As New. Unread book in perfect condition.
Lingua: Inglese
Editore: CreateSpace Independent Publishing Platform, 2017
ISBN 10: 1978304870 ISBN 13: 9781978304871
Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 33,34
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Aggiungi al carrelloCondizione: New.
Lingua: Inglese
Editore: Createspace Independent Publishing Platform, 2017
ISBN 10: 1978304870 ISBN 13: 9781978304871
Da: THE SAINT BOOKSTORE, Southport, Regno Unito
EUR 33,35
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Aggiungi al carrelloPaperback. Condizione: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.
Lingua: Inglese
Editore: Createspace Independent Publishing Platform, 2017
ISBN 10: 1978304870 ISBN 13: 9781978304871
Da: CitiRetail, Stevenage, Regno Unito
EUR 38,08
Quantità: 1 disponibili
Aggiungi al carrelloPaperback. Condizione: new. Paperback. What is a MEMORYLESS predictive model? Markov models are a powerful predictive technique used to model stochastic systems using time-series data. They are centered around the fundamental property of "memorylessness", stating that the outcome of a problem depends only on the current state of the system - historical data must be ignored. This model construction may sound overly simplistic. After all, if you have historical data why not use it to develop more complete and well-informed models? Surely, it would lead to more accurate predictions. However, when modelling time-series data where previous results are of limited relevance, a memoryless model delivers vast performance advantages. By considering only the present state, algorithms become highly scalable, stable, fast and, above-all-else, extremely versatile. Speech recognition is a perfect example - nearly all of today's speech recognition algorthms are built using Markov Models. In this book we will explore why a Memoryless predictive model can be so advantageous to the modern tech industry. We will take a look at fundamental mathematics and high-level concepts alike, extending our understanding of the subject beyond the simple Markov Model. You will learn. Foundations of Markov ModelsMarkov ChainsCase Study: Google PageRankHidden Markov ModelsBayesian NetworksInference Tasks This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.