Neural networks traditionally refer to a network or circuit of biological neurons. The modern usage of the term often refers to artificial neural networks (ANN), which are composed of artificial neurons or nodes - programming constructs that mimic the properties of biological neurons. ANN are ...

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9 views

NARX model to predict future values

I have this problem , where I have to predict a value of a indicator which depends on 270 other predictor variables. I read the time series modelling and prediction on MATLAB , which took the example ...
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1answer
22 views

Very different Neural Network test errors for same architecture

So I'm doing a time series prediction, and assessing the capability of the ANN to predict that time series. I am using Matlab's neural network toolbox functions, and the training parameters are the ...
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0answers
36 views

What is a convolutional neural network

I have been studying neural networks and I recently found out about deep learning and convolutional neural networks. Can someone give me a newbie introduction to convolutional neural networks, what ...
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1answer
19 views

How well do Convolutional neural networks in other image domains?

I was recently trying out caffe and learning about CNN. So far I have seen that the model used by Krizhevsky performs really well in natural images. However I wanted to know how these models or CNN ...
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0answers
11 views

unary classification in PyBrain

I've just started using PyBrain for some data classification work, and I've gotten it working pretty well where I have data from all possible classes and I can train the network using all the classes. ...
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0answers
28 views

What kind of weight values should a restricted Boltzmann machine have?

I designed a Gaussian (Gaussian distributed visible layer) - Bernoulli (binary distributed) RBM model (for reference, see: Improved Learning of Gaussian-Bernoulli Restricted Boltzmann Machines, pdf) ...
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0answers
40 views

How to apply T test to fMRI data [closed]

I have a fMRI data, the size of which is 1210*50000. And I also have a regressor matrix, the size of which is 8*1210, each row corresponding to one condition,the value of the regressor matrix is ...
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0answers
10 views

Neural Network & Image recognition - preprocessing

I have a project where I´m supposed to recognize ordinary data written numbers (1-10) from images as well as some geometrical shapes (rectangular, triangle, circle, etc.). However, I´m not sure about ...
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2answers
49 views

What does the convolution step in a Convolutional Neural Network do?

I am studying convolutional neural networks (CNNs) due to their applications in computer vision. I am already familiar with standard feed-foward neural networks, so I'm hoping that some people here ...
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0answers
40 views

disadvantages of Neural network method

Hello Dear Researchers! I want to list the advantages and disadvantages of Neural network methods for classification or estimation purposes. I have already found the advantages of NN method in many ...
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1answer
29 views

Neural Net Hidden Layer

I have done some searching on the topic and I know the answer is mainly "it depends". BUT, I haven't found anything stating general relationships regarding size of data and number of hidden layers. ...
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1answer
13 views

Tanh activation function and sparsity constraint

According to Lecun's paper "effient backprop" [1] the tanh activation function should be preferred over the logistic activation function for the hidden units in neural networks. For the tanh units ...
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0answers
9 views

What should be the fitness function while using Particle Swarm optimisation

I am using Particle Swarm Optimisation for optimising the parameters of a Neural network (for multi-class classification problem). But what should be the fitness function for it ? I have tried ...
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1answer
18 views

Requirements for a valid neural network activation function?

What rules define a valid neural network activation function, excluding biological plausibility? What set of principles do softmax, rectified linear units, hyperbolic tangent, sigmoid, etc. follow? ...
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22 views

repeating rare examples in unbalanced data classification

So I'm trying to train a neural network for a rare event detection. based on that i have like 1000 times more examples for non-target (everything else) examples that i have for target examples. So i ...
2
votes
1answer
27 views

Comprehensive list of activation functions in neural networks with pros/cons

Are there any reference document(s) that give a comprehensive list of activation functions in neural networks along with their pros/cons (and ideally some pointers to publications where they were ...
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0answers
16 views

Why can't I approximate sinusoid with tanh activation function?

I've been playing with this applet for learning about backpropagation: http://neuron.eng.wayne.edu/bpFunctionApprox/bpFunctionApprox.html What continues to confuse me, is why I can't approximate ...
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0answers
41 views

Logistic Regression, SVM or NN?

Just attended Andrew Ng’s online course on ML and although I’ve understood the methods I seem to be missing the intuition on where to apply them in terms of classification problems. What are the ...
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0answers
10 views

Neural Network hidden layer proportionality

I am working on Proportionality on Multiple Hidden layer . My application area is Handwritten Pin code Recognition. I had used five hidden layer for better accuracy. I know time complexity will ...
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2answers
78 views

Newbie to neural networks

Just starting to play around with Neural Networks for fun after playing with some basic linear regression. I am an English teacher so don't have a math background and trying to read a book on this ...
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4answers
78 views

What is the difference between convolutional neural networks, restricted Boltzmann machines, and auto-encoders?

Recently I have been reading about deep learning and I am confused about the terms (or say technologies). What is the difference between convolutional neural networks (CNN), restricted Boltzmann ...
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0answers
30 views

Best Validation check number for MATLAB neural network

I'm using 10-fold cross validation and patternent function for a binary classification problem in ...
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0answers
18 views

Best mathematical or experimental solution for number of layers and neurons in neural network structures

I'm designing a neural network for binary classification problem in MATLAB. I have different number of inputs and samples in every problem ( I designed a software ...
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0answers
17 views

All neural network designs stop because of early stopping in MATLAB

I'm using patternnet for my binary classification in MATLAB and using ...
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0answers
42 views

Best K in K-fold cross validation

I'm using K-fold cross validation technique for generating train,test and ...
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0answers
18 views

The ethics of using an optimal multiclass feature set for binary classification

I'm currently trying to find the best feature set/network architecture configuration for a binary classification problem, however to approach it via the usual means of building and testing does not ...
2
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0answers
99 views

Finding best neural network structure and inputs using optimization algorithm and cross-validation

I'm using optimization algorithm to find best structure+inputs of a patternnet neural network in MATLAB R2014a using ...
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3answers
85 views

How to Use Neural Networks to Forecast Time Series Data with Predictor Variables?

I have browsed a lot of topics here, but the ones I see were all about forecasting a single variable, depending on its historical values. Whereas I want to predict a variable, by estimating a ...
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1answer
28 views

Composition of bankruptcy probability and firm size

I'm using neural network for a binary classification problem of bankruptcy prediction using patternnet function in MATLAB, so i ...
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0answers
15 views

How to derive the gradient formula for the Maximum Likelihood in RBM?

I am learning RBM (restricted Boltzmann machine) for deep learning. The log-likelihood of RBM is given as : and its gradient w.r.t. the parameter is: I don't understand how is the gradient ...
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1answer
92 views

What's the relation between deep learning and extreme learning machine?

Often I have found deep learning and extreme learning machine discussed together. Based on my little knowledge of the subject my impression is that they are different methods with different aims. ...
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2answers
45 views

Test data results does not match with cross validation results

I'm confused with my data I'm currently playing with. I have a data set which holds 58 attributes in 10000 instances. Attributes are 56 float values typically within 0 to 1. Then there is nominal ...
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0answers
14 views

NN: Should we apply weight decay to the bias?

In CS294A lecture notes, Andrew Ng writes (about autoencoders): "Usually weight decay is not applied to the bias terms... Applying weight decay to the bias units usually makes only a small different ...
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2answers
16 views

Epoch vs Incremental Update over Entire Dataset

In the context of neural networks, what is the difference between training like this: ...
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0answers
14 views

Multilayer perceptron 3 classes

I need to create a MLP for 3 classes. I don't know to to do the structure. I thought that having one hidden layer (don't know how many nodes) and and output layer with 2 nodes would be enough, like ...
0
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0answers
27 views

Adaboost for neural networks. Is it still worth it?

I have a question about Adaboost and neural networks. Given the recent development in neural networks (dropout, maxout, or rectified linear units) is there a significant benefit of performing Adaboost ...
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0answers
52 views

how to calculate Root Mean Square Error (RMSE) for predicted Probability Density Function (PDF) in Matlab

I have used Mixture Density Networks for probability density function prediction. I am wondering how I can calculate Root Mean Square Error (RMSE) of predicted pdf in MATLAB. Thanks.
3
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2answers
73 views

Linear post-treatment of nonlinear regression

I have often found in practice, using nonlinear regression techniques such as feedforward neural nets or random forests, that the resulting actual-vs-fitted plot (on training set) seems obviously ...
0
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0answers
8 views

Function domain as a problem for linear output unit

I'm doing some regression using neural net(using MLP implementation from http://deeplearning.net/tutorial/mlp.html, I used my own but it produced the same results before I opted for this one), ...
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2answers
182 views

Does feature standardization always make sense?

I wonder if feature scaling like this makes always sense for neural networks: Let $T$ be the training set and $x_i \in \mathbb{R}^n$ with $d_i \in T$ be the feature vector of $d_i$. Then add another ...
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2answers
44 views

Does the activation function of output layer differ during training and already trained network?

I'm creating and OCR app, and so far it seams to work. It's quite similar to example from Coursera - Machine learning course. Output layer of network has as many neurons as classes needed to ...
1
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0answers
17 views

Applying autoencoders for dimensionality reduction in audio: Why does this create a low-pass effect? [closed]

I've been playing around with framing audio data and training a single-layer autoencoder to find a dimensionality-reduced form (say 128-sample frames to 32-dimension frames). When I test the audio ...
0
votes
1answer
55 views

Convert continues number to integer number in optimization algorithms in MATLAB

I'm using a continuous optimization algorithm for optimizing neural network's number of neurons in first and second layers besides feature selection so I used this structure for converting continues ...
1
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0answers
17 views

Is NN saturation always bad?

I am trying to analyse the effect of hidden unit saturation (outputting mostly 0 and 1 for sigmoid, and not much in-between) on the neural network training performance, and I feel a bit stuck, ...
1
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0answers
39 views

Pre-training deep neural networks by supervised learning

When pre-training deep neural networks layer by layer, is it normal to pre-train the layers -which haven't been pre-trained by unsupervised training- by using supervised training before we train the ...
0
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0answers
22 views

auto-steering using neural networks

I was hoping if anyone could point me in right direction, I want to implement a neural network that could steer an autonomous car, I have implemented basic classification problems before using single ...
0
votes
1answer
18 views

Reported error rates on neural networks

It is common to depict the error rates of types of neural networks in a table, for example, see the MNIST website. However, because of the non-determinism caused by weight initialization the actual ...
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0answers
28 views

How calculate average probabilities in MLP or SVM?

I have a system that find best model (best inputs and parameters of MLP/SVM) model in a financial problem for every inserted database and create a specific model for a specific data sample. I'm using ...
1
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0answers
36 views

Neural Networks and Picture Recognition

I have spent a bunch of time looking at this series of videos (Neural Network Tutorial), by Ryan Harris: https://www.youtube.com/watch?v=Q_5B3GuWPCc&index=41&list=PL29C61214F2146796 I am ...
0
votes
1answer
64 views

Feeding a layer from a deep-learnt neural network into an SVM

In http://jmlr.org/proceedings/papers/v32/donahue14.pdf, it is stated: Our top-performing method (based on validation accuracy) trains a linear SVM on DeCAF6 Can you delineate in a way ...