Binary options neural network

Binary options neural network

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Neural network - binary vs discrete / continuous input - Cross Validated

Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization. It only takes a minute to sign up. Connect and share knowledge within a single location that is structured and easy to search. Of course, I'm only talking about inputs that could be transformed into either form; e. And the assumption is that the range of possible values would be the same for all input nodes.

See the pics for an example of both possibilities. While researching on this topic, I couldn't find any cold hard facts on this; it seems to me, that - more or less - it'll always be "trial and error" in the end.

Of course, binary nodes for every discrete input value mean more input layer nodes and thus more hidden layer nodesbut would it really produce a better output classification than having the same values in one node, with a well-fitting threshold function in the hidden layer? Would you agree that it's just "try and see", or do you have another opinion on this?

Whether to convert input variables to binary depends on the input variable. You could think of neural network inputs as representing a kind of "intensity": i. This setup does not make sense for categorical variables.

For real-valued variables, just leave them real-valued but normalize inputs. say you have two input variables, one the animal and one the animal's temperature. Yes there are. Imagine your binary options neural network is to build a binary classifier. Then you model your problem as estimating a Bernoulli distribution where, given a feature vector, the outcome belongs to either one class or the opposite.

The output of such a neural network is the conditional probability. If greater than 0. I also faced same dilemma when I was solving a problem. I didn't try both the architecture, but my binary options neural network is, if the input variable is discrete then the output function of neural network will have the characteristic binary options neural network impulse function and neural network is good at modeling impulse function, binary options neural network.

In fact any function can be modeled with neural network with varying precision depending on complexity of neural network. The only difference is, in first architectureyou have increase the number of input so you more number of weight in first hidden layer's node to model the impulse function but for the second architecture you need more number of node in hidden layer compared to first architecture to get same performance.

Sign up to join this community. The best answers are voted up and rise to the top. Stack Overflow for Teams — Collaborate and share knowledge with a private group, binary options neural network. Create a free Team What is Teams? Learn more, binary options neural network. Asked 5 years, 11 months ago. Active 2 years, 4 months ago. Viewed 22k times. Improve this question.

edited Jan 20 '19 at asked Jun 21 '15 at cirko cirko 1 1 gold badge 4 4 silver badges 13 13 bronze badges. Add a comment. Active Oldest Votes. Improve this answer. Destruktor 4 4 bronze badges. answered Mar 24 '16 at Matt Matt 1 1 silver badge 5 5 bronze badges. Well I think it's obvious that nominal scales can't be "calculated" or represented by a function. Regarding real values, like you I tend to think that real values might be "better" than "classified" real values due to the smoother tranisitions, but I just binary options neural network find any hard proof on that.

Seems like another case of "trial and error" to me. answered Jun 21 '15 at jpmuc jpmuc But in my question, binary options neural network, I wanted to refer to normalized discrete values of a certain range, i.

if the inputs could be within a range, then all of the nodes should have the same range, i, binary options neural network. be normalized. In that case, would it still be preferrable to use binary binary options neural network for each discrete value?

edited Mar 23 '17 at G5W 2, 1 1 gold badge 8 8 silver badges 21 21 bronze badges. answered Sep 6 '16 at Anshu Abhishek Anshu Abhishek 11 1 1 bronze badge. Sign up or log in Sign up using Google. Sign up using Facebook. Sign up using Email and Password. Post as a guest Name. Email Required, but never shown. The Overflow Blog. Featured on Meta.

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Binary Neural Networks

/10/11 · Nexus Indicator and again neural networks in binary options trading. Marketers are not appeased, and now in any product you can find the mark "neural networks". Now, it’s not enough for us that the indicator is not repainting and without delay, we still need it to be able to adapt to an ever-changing marketReviews: 13 /06/21 · The output of such a neural network is the conditional probability. If greater than you associate it to a class, otherwise to the other one. In order to be well defined, the output must be between 0 and 1, so you choose your labels to be 0 and 1, and minimize the cross entropy, E = y (x) t 知乎，中文互联网高质量的问答社区和创作者聚集的原创内容平台，于 年 1 月正式上线，以「让人们更好地分享知识、经验和见解，找到自己的解答」为品牌使命。知乎凭借认真、专业、友善的社区氛围、独特的产品机制以及结构化和易获得的优质内容，聚集了中文互联网科技、商业、影视