Does my machine learning approach make sense

Hey, So I want to do the following: I have multivariate time series data from sensors. I want to feed the raw, unlabeled data into a deep learning model (Thinking of deep belief nets right now). I hope that in the output layer of the rbm, there will be features of the time series. With every additional rbm, i will learn features of higher level . Those features will be used to represent the time series. Now some questions: 1) In such a case, how do you construct the input ? im thinking that every visible unit is a part of one sensor signal (with window size w). 2) Is a feature in the output an actual part of the time series? Or am I misunderstanding this. 3) Does anyone…


Link to Full Article: Does my machine learning approach make sense

Pin It on Pinterest

Share This

Join Our Newsletter

Sign up to our mailing list to receive the latest news and updates about homeAI.info and the Informed.AI Network of AI related websites which includes Events.AI, Neurons.AI, Awards.AI, and Vocation.AI

You have Successfully Subscribed!