So after you have a machine learning algorithm trained, with your layers, nodes, and weights, how exactly does it go about getting a prediction for an input vector? I am using MultiLayer Perceptron (neural networks). From what I currently understand,
http://en.wikipedia.org/wiki/Perceptron#Example My question is, why are there 3 input values in each vector when NAND only takes 2 parameters and returns 1: http://en.wikipedia.org/wiki/Sheffer_stroke#Definition Pasted code for your convenience: th =
I want to create a scatter plot of handwritten digits of 0 and 1 (http://yann.lecun.com/exdb/mnist/). I took 4 samples ie two 0's and two 1's. Each handwritten digits are having pixel values having dimension 1 cross 778 matrix. Now I want to do scatt
I am moving my first steps in neural networks and to do so I am experimenting with a very simple single layer, single output perceptron which uses a sigmoidal activation function. I am updating my weights on-line each time a training example is prese
I'm having trouble seeing what the threshold actually does in a single-layer perceptron. The data is usually separated no matter what the value of the threshold is. It seems a lower threshold divides the data more equally; is this what it is used for
I'm experimenting with single-layer perceptrons, and I think I understand (mostly) everything. However, what I don't understand is to which weights the correction (learning rate*error) should be added. In the examples I've seen it seems arbitrary. --
I want to train a neural network with the help of Hadoop. We know when training a neural network, weights to each neuron are altered every iteration, and each iteration depends on the previous. I'm new to Hadoop and don't quite familiar with features
So there is grate sample (only one real sample we found). And it is quite limiting. It shows how to create an architecture of artificial neutral network where all neurons of one layer are connected (forward) to all neurons of following (next) layer.
So here is shown a simple example - 2 floats as data + 1 float as output: Layer 1: 2 neurons (2 inputs) Layer 2: 3 neurons (hidden layer) Layer 3: 3 neurons (hidden layer) Layer 4: 1 neurons (1 output) And we create ANs with something like cvSet1D(
I need an example of Multi-layer Perceptron (with at least 3 layers) C/C++ programm using OpenCV. Where can I get one? --------------Solutions------------- Here: http://opencv.willowgarage.com/documentation/cpp/neural_networks.html?
I am coding a perceptron to learn to categorize gender in pictures of faces. I am very very new to MATLAB, so I need a lot of help. I have a few questions: I am trying to code for a function: function [y] = testset(x,w) %y = sign(sigma(x*w-threshold)
after running the perceptron code in Matlab I get the following weights: result= 2.5799 2.8557 4.4244 -4.3156 1.6835 -4.0208 26.5955 -12.5730 11.5000 If i started with these weights : w = [ 1 1 1 1 1 1 1 1 1]'; How do I plot the line that separates t
Hi I'm pretty new to Python and to NLP. I need to implement a perceptron classifier. I searched through some websites but didn't find enough information. For now I have a number of documents which I grouped according to category(sports, entertainment
I've been reading some online tutorials about Neurons, Percepton and Multi Layer Perceptron concepts. Now, I would like to implement the concept in my own examples. What I would like to do is to implement the following simple algorithm into my networ
For the implementation of single layer neural network, I have two data files. In: 0.832 64.643 0.818 78.843 Out: 0 0 1 0 0 1 The above is the format of 2 data files. The target output is "1 for a particular class that the corresponding input belongs
I was reading on perceptrons and trying to implement one in haskell. The algorithm seems to be working as far as I can test. I'm going to rewrite the code entirely at some point, but before doing so I thought of asking a few questions that have arose
I'm having sort of an issue trying to figure out how to tune the parameters for my perceptron algorithm so that it performs relatively well on unseen data. I've implemented a verified working perceptron algorithm and I'd like to figure out a method b
Is there existing software for discriminative reranking, such as that used by the Charniak NLP parser, Shen, Sarkar, and Och's parser or Shen and Joshi's techniques? I'd like something that I can easily adapt for my own uses, which are similar to par
When designing a feed forward neural network with multiple outputs, is there a conceptual difference (other than computational efficency) between having a single network with multiple outputs, and having multiple networks, each having a single output
Here is my perceptron implementation in ANSI C: #include <stdio.h> #include <stdlib.h> #include <math.h> float randomFloat() { srand(time(NULL)); float r = (float)rand() / (float)RAND_MAX; return r; } int calculateOutput(float weigh