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Tag Archive for machine-learning

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):

Algorithm to analyse and predict