Decision trees vs. Neural Networks
I’m implementing a machine learning structure to try and predict fraud on financial systems like banks, etc… This means that there is a lot of different data that can be used to train the model eg. card number, card holder name, amount, country, etc…
What’s the normal way machine-learning algorithms are integrated into normal programs?
I’m currently taking a machine learning course for fun, and the course heavily focuses on Matlab/Octave to write the code. One thing mentioned in the course is that, while Matlab/Octave are great for prototyping, they’re very rarely used for production algorithms. Instead, those algorithms are typically rewritten in C++/Python/etc., using appropriate libraries, before reaching customers.
Using machine learning to aim mirrors in a solar array?
I’ve been thinking about solar collectors where several independent mirrors to focus the light on a solar collector, similar to the following design from Energy Innovations.
What math skills are required to learn machine learning? [closed]
Closed 9 years ago.
Tasks incorrectly categorized with online text classifiers
Context: Finding company to do the job
Machine Learning With Categorical and Continuous Data
This question could go here or on S.O. perhaps…
Clustering algorithm that doesn’t require predefining the number of clusters required
I have a set of user queries from a search engine that I want to cluster. The only clustering algorithm I have come across so far is the K-means clustering algorithm, which requires defining the number of clusters up front. But in this case, I do not know how many clusters exist in the data. Is there any clustering algorithm that performs clustering without predefining the number of clusters?
Can a neural network provide more than “yes” or “no” answers?
Every example neural network for image recognition I’ve read about produces a simple “yes” or “no” answer. One exit node corresponds to “Yes, this is a human face,” and one corresponds to “No, this is not a human face.”
Machine Learning with sample data set
I have a question regarding Machine Learning in general. Consider the following scenario:
Machine Learning Algorithm for Heating/Lighting Optimization
I’m working on a project where I’m developing an interface that learns how you typically use a space, and tries to create the most appropriate control strategy for heating/lighting. I’ve done some research into the area of machine learning techniques, but I was wondering if there were any recommendations on which learning algorithm would work best for this scenario. I have a lot of different input parameters: I designed a low-cost wireless sensor which reports light, temperature, humidity, and motion detection every 8 seconds… I also tap into live weather feeds through the internet for exterior conditions… And I’m also storing all of the different UI changes (toggles, sliders, etc…) so hopefully I can tell when people are actually changing certain settings and adapt accordingly. As far as learning algorithms… there’s a lot of different options including (to name just a few):