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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2answers
32 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
38 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 ...
0
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1answer
21 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. ...
0
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1answer
12 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
7 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 ...
0
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1answer
14 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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0answers
19 views

Does Matlab neural network training speed up with a gpu [on hold]

I read that Matlab neural network training does not support GPU processing when using trainlm Matlab neural net training on GPU. Considering I use trainscg, how much faster can I expect the the ...
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0answers
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
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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
39 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
9 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 ...
3
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2answers
72 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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0answers
21 views

What are the difference between CNN, RBM,and auto-encoder?

Recently I am learning about deep learning and I am confused between the terms (or say technologies). What are the different between convolution neural network (CNN), restricted Boltzmann Machines ...
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0answers
25 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
15 views

All neural network designs stop because of early stopping in MATLAB

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

Best K in K-fold cross validation

I'm using K-fold cross validation technique for generating train,test and ...
0
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0answers
16 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
93 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 ...
0
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3answers
75 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 ...
0
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1answer
24 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 ...
0
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0answers
14 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
67 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. ...
0
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2answers
43 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 ...
0
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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: ...
0
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0answers
13 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 ...
0
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0answers
45 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
votes
2answers
72 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), ...
0
votes
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 ...
1
vote
2answers
43 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
15 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
53 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
16 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
36 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 ...
0
votes
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
35 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
63 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 ...
0
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0answers
11 views

How do current image-to-3D model systems work?

I understand that at least one system for automatically modeling 3D objects from image data exists. Autodesk appears to have developed a good method. Does anyone know the basic structure and ...
1
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0answers
37 views

How to give an input when you are using Machine Learning method in R

I am new to R and machine learning algorithms. I have basic knowledge of different machine learning algorithms. I have four years of daily sales data.I am trying to predict sales using Support Vector ...
2
votes
2answers
35 views

Regression vs Multiclassification

I was working with SVR, and wondering, why can't I solve a natural regression problem as a multiclassification task ? Example: I have for a regression problem: targets 1, 5 and 10, trying to fit ...
0
votes
1answer
19 views

output of a non linear neuron

If a neuron uses a non linear activation function such as a sigmoid function, then the output of that neuron can be any value between 0 and 1. suppose if the activation function results in value like ...
0
votes
1answer
22 views

Difference between a non-linear neuron vs non-linear activation function

I need to know the difference between a non-linear neuron vs non-linear activation function AND linear neuron vs linear activation function.
0
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0answers
21 views

Custom Neural network in matlab

I am trying to make a custom Neural network structure using 'network' command. I am a little confused.Can we change the connections between individual neurons?Like it is possible in weka?
0
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0answers
43 views

MATLAB interperetation of Neural networks

I am new to Neural networks and I am trying to build a custom neural network using the NN toolbox in MATLAB.I am using the "create custom neural network function". Now, I find the neural network ...