You hear a lot about machine learning these days. But how does it actually work?
Learn how to use machine learning algorithms and sensors on your mobile device to track your activity! This MATLAB example provides step-by-step instructions.
From my favorite matlab blog of AngelJohnsy (click to view the rest of the article).
Clustering can be defined as the grouping of data points based on some commonality or similarity between the points. One of the simplest methods is K-means clustering. In this method, the number of clusters is initialized and the center of each of the cluster is randomly chosen. The Euclidean distance between each data point and all the center of the clusters is computed and based on the minimum distance each data point is assigned to certain cluster. The new center for the cluster is defined and the Euclidean distance is calculated. This procedure iterates till convergence is reached…
From the STEM Academy of Elements 14 community (click here).
Watch this video!
Just watch this video, from Matlab ‘s you tube channel:
Perfect for the beginner, this demo illustrates simple object detection (segmentation, feature extraction), measurement, and filtering. Requires the Image Processing Toolbox (IPT) because it demonstrates some functions supplied by that toolbox, plus it uses the «coins» demo image supplied with that toolbox. If you have the IPT (you can check by typing ver on the command line), you should be able to run this demo code simply by copying and pasting this code into a new editor window, and then clicking the green «run» triangle on the toolbar.
Draw some funny and useful parametric curves in Matlab. It’s easy! Click here!
Interested in image processing? Check out these MATLAB tips!