Octave : logistic regression : difference between fmincg and fminunc

algorithm machine-learning neural-network octave
hqt picture hqt · Aug 24, 2012 · Viewed 42.7k times · Source

I often use fminunc for a logistic regression problem. I have read on web that Andrew Ng uses fmincg instead of fminunc, with same arguments. The results are different, and often fmincg is more exact, but not too much. (I am comparing the results of fmincg function fminunc against the same data)

So, my question is : what is the difference between these two functions? What algorithm does each function have implemented? (Now, I just use these functions without knowing exactly how they work).

Thanks :)

Answer

carandraug picture carandraug · Aug 24, 2012

You will have to look inside the code of fmincg because it is not part of Octave. After some search I found that it's a function file provided by the Machine Learning class of Coursera as part of the homework. Read the comments and answers on this question for a discussion about the algorithms.

Related questions

Training a Neural Network with Reinforcement learning

I know the basics of feedforward neural networks, and how to train them using the backpropagation algorithm, but I'm looking for an algorithm than I can use for training an ANN online with reinforcement learning. For example, the cart pole …

Read More
Perceptron learning algorithm not converging to 0

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 …

Read More
A simple explanation of Naive Bayes Classification

I am finding it hard to understand the process of Naive Bayes, and I was wondering if someone could explain it with a simple step by step process in English. I understand it takes comparisons by times occurred as a …

Read More