0
votes
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? ...
0
votes
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 ...
3
votes
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 ...
0
votes
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 ...
0
votes
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 ...
1
vote
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
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 ...
1
vote
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 ...
1
vote
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 ...
0
votes
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
votes
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 ...
0
votes
0answers
37 views

Neural-Net style pattern recognition with an unknown/varying number of inputs?

Say for example I had a weighted graph such that each node had an associated value. The nodes' values are given by some function of the edge weights and the number of edges as well as the node's ...
0
votes
0answers
45 views

Algorithm for online handwriting recognition

Is there any specific algorithm for online handwriting recognition? The algorithm should recognize non-cursive and cursive handwriting. I know there is already a similar post on stackoverflow.com, ...
1
vote
1answer
97 views

Does the vanishing gradient in RNNs present a problem?

One of the often cited issues in RNN training is the vanishing gradient problem [1,2,3,4]. However, I came across several papers by Anton Maximilian Schaefer, Steffen Udluft and Hans-Georg Zimmermann ...
0
votes
0answers
128 views

10 fold cross validation model in weka

Trying to build a specific Neural Network arcitecture and testing it using 10 fold cross validation of a dataset. Now building the model is a tedious job and Weka expects me to make it 10 times for ...
0
votes
0answers
18 views

Forensics in wireless networks, anomaly detection and beyond?

first i'de like to apologize if this is not the right place. Next year i'm gonna be working on my final project in computer security, i have to build a wireless forensics tool that can analyse a data ...
0
votes
1answer
34 views

How to : a brief intro to scaling and rescaling data ( inputs) for supervised learning algorithms

I understand the concept of scaling and that it improves results in SVM's and NN's. however I would like to find somewhere where is is explained, in easy "layman's terms" terms. of how it is done. I ...
1
vote
0answers
25 views

Bayesian Perceptron - how can I generate many different perceptrons?

I am going to implement the Bayesian version of a perceptron that I read in the Statistical Mechanics of learning, by Engel-Van Den Broeck. The idea to improve the performance is to use many Gibbs ...
2
votes
1answer
75 views

Image processing with neural network

I am trying to learn how Neural Network works on image recognition.I am confused that how neural network that how i will give input.My defination is find(track) object in squence of images(particular ...
0
votes
1answer
62 views

Question about normalize/scale data before using neuralnet

I have read several threads about the issue on same outputs after people fitting a neural network model with R neuralnet. Posted Solution is to normalize or scale the data before fitting model. Since ...
0
votes
0answers
23 views

How Sensitive Are Neural Networks?

CrossPost: https://stackoverflow.com/questions/24301472/how-sensitive-are-ff-neural-networks I am aware of pruning, and am not sure if it removes the actual neuron or makes its weight zero, but I am ...
0
votes
0answers
23 views

Good way to use adaptive learning rates in neural network

Adaptive learning rates means using different learning rate for different weight in neural network. Except for the emperical method which updates these learning rates based on consistency in gradient, ...
0
votes
0answers
28 views

Sparse ELM vs SVM

What's the difference between SVM and Sparse Extreme Learning Machine with Gaussian kernel proposed in the following paper:http://www.ntu.edu.sg/home/egbhuang/pdf/Sparse-ELM-IEEE-T-Cybernetics.pdf As ...
2
votes
1answer
44 views

Having a Neural Network recreate what it's learned

I've created a basic Neural Network that learns from basic information and can verify whether or not a piece of information matches it's parameters from a match percentage. Conceptually however, I ...
0
votes
0answers
13 views

LMS cost function vs cross entropy cost function in neural networks

What is difference between using various cost functions: LMS,Cross entropy in neural networks? All of them have same derivative w.r.t final activation and hence all the gradients are still gonna ...
1
vote
1answer
329 views

tanh activation function vs sigmoid activation function

tanh activation function is nothing but 2*sigmoid - 1. Does it really matter between using those two activation functions. Which function is better in which cases?
0
votes
0answers
27 views

Time and space complexity of Deep Belief Nets (DBN)

What is the time and space(memory) complexity of DBNs? given d:number of dimensions(attributes), n:number of records, and l:number of hidden layers.
0
votes
0answers
30 views

Supervised classification on different time series

I have 300 files, each file has a time series data with a class label(0 or1) for each data point.I want to build a classifier, which can predict the class of a new time series data. How should I ...
0
votes
1answer
56 views

neural network output layer for binary classification

I'm using a neural network for a binary classification problem. Is it better to have one neuron in the output layer or to use two, i.e. one for each class?
0
votes
0answers
40 views

Introduction to recurrent neural networks?

I have two questions: 1- What are the applications of recurrent neural networks? 2- Can you recommend some good resources/papers/tutorials that introduce recurrent neural networks?
4
votes
1answer
77 views

Where can I find an implementation of Hinton's original Boltzmann Machine?

I've been trying to implement the Boltzmann machine 4-2-4 encoder that appeared in A Learning Algorithm for Boltzmann Machines. but I am unable to find clear pseudocode for doing it or more specific ...
2
votes
2answers
88 views

Trouble training Neural Network

I'm trying to use Encog to define an artificial neural network in order to process this dataset (6 inputs, 2 yes/no outputs), but I can't get any lower than ~65% error. The NN is feedforward with ...
0
votes
1answer
186 views

Explanation of the Regression Plot in the Matlab Neural Network Toolbox

What does the Regression Plot in the Matlab Neural Network Toolbox show? I thought I understood it when I looked at a univariate regression plot, but I've just plotted one for multivariate regression, ...
0
votes
0answers
18 views

Why should the feature be standardized before feeding to the neural network algorithm [duplicate]

Before feeding the features to the neural network algorithm, we have to standardize these features. Why? This is an interview question asked in my recent interview for a data scientist. Can ...
1
vote
0answers
38 views

How should I handle variables whose data points have varying degrees of predictive power?

I'm trying to determine which type of learning algorithm is best for making predictions on my data. My data set consists of several independent variables, each of which is accompanied by an ...
0
votes
0answers
32 views

Is there a neural network r package for mixed model?

R neural network package such as nnet does not allow to specify random variables. I have a dataset with repeated measures of the same subject, which introduce random effects as in a general linear ...
0
votes
0answers
45 views

3D space learning and prediction

I want suggestions about learning and predicting some object's position before hitting one out of four sides of a wall. I have some priority according to side of wall, and of course all the scenarios ...
5
votes
3answers
267 views

What are alternatives of Gradient Descent?

Gradient Descent has a problem of getting stuck in Local Minima. We need to run gradient descent exponential times in order to find global minima. Can anybody tell me about any alternatives of ...
0
votes
1answer
30 views

Neural Network Process Question - Updating weights after each training set

When creating a neural network, do I update the weights after each run of forward then back propogation? Or do I just keep the random weights and update the Delta variables? I am looking at slide 8 ...
0
votes
0answers
27 views

Neural Networks - Calculating delta in Backpropogation

I'm developing software to create a neural network. I have the forward propogation code done, but when I started working on the algorithm for back-propogation I ran into a problem. I'm having ...
1
vote
2answers
56 views

What are problems of many hidden layers?

Is there any problem if we use too many hidden layers in Neural Network? Can anyone simply describe what problems can occur if we have too many hidden layers.
0
votes
1answer
140 views

L1-norm cost function for Neural Network. (Regression)

I am trying to build a regression model using a neural network. The final cost measure is the mean absolute error (MAE) on the output (one output unit, 200 input units). Right now all my hidden units ...
2
votes
1answer
56 views

Neural Network: What if there are multiple right answers for a given set of inputs?

For a given input into the input nodes, there are multiple correct values for the output nodes. In the training set, there are times when the inputs result in a certain output, and other times when ...
0
votes
3answers
154 views

Neural network packages which allow shared weights and parallel training

I'm curious if there are any neural network packages out there that easily allow one to construct feed forward neural networks with shared weights, but also allow for the training to be done in ...
1
vote
1answer
70 views

Computational Complexity of Prediction using SVM and NN?

I've seen answers discussing the complexity of training SVMs and neural nets, but how about for predicting new responses once a model has been trained? For context, I'm working on an app that should ...
2
votes
1answer
370 views

Difference between Bayes network, neural network, Petri Nets and decision tree

What is the difference between Neural network, Bayesian network, Decision tree and Petri Nets eventhough they are all graphical models and visually depict cause-effect relationship. Thank you
1
vote
0answers
37 views

How to derive errors in neural network with the backpropagation algorithm?

From this video by Andrew Ng around 5:00 How are $\delta_3$ and $\delta_2$ derived? In fact, what does $\delta_3$ even mean? $\delta_4$ is got by comparing to y, no such comparison is possible ...
-1
votes
1answer
110 views

Is it OK to increase validation checks and decrease min gradient while training neural network?

My input vector is a 130*85 matrix and my target vector is 130*26 matrix. I am using the below parameters for training the network with 60 hidden nodes. ...
0
votes
0answers
49 views

Tutorial on Radial Basis Function Networks?

I want to learn about Radial Basis Function Neural Networks, can you please suggest a good introduction or tutorial? All the introductions I found are rather short or incomplete or so.
0
votes
1answer
57 views

Decision boundary equation of the perceptron

As I know the standard linear equation has the following form in $R^2$: $w_1 x_1 + w_2 x_2 = b$ where $b$ is the intercept with $x_2$ Also I know that a decision boundary in $R^2$ for a perceptron ...