LIONbook Chapter 14: Selforganizing maps
The LIONbook on machine learning and optimization, written by cofounders of LionSolver software, is provided free for personal and nonprofit usage. Chapter 14 looks at Selforganizing maps.
Here is the latest chapter from LIONbook, a new book dedicated to "LION" combination of Machine Learning and Intelligent Optimization, written by the developers of LionSolver software, Roberto Battiti and Mauro Brunato.
This book is freely available on the web.
Here are the previous chapters:
 Chapters 12: Introduction and nearest neighbors.
 Chapter 3: Learning requires a method
 Chapter 4: Linear models
 Chapter 5: Mastering generalized linear leastsquares
 Chapter 6: Rules, decision trees, and forests
 Chapter 7: Ranking and selecting features
 Chapter 8: Specific nonlinear models
 Chapter 9: Neural networks, shallow and deep
 Chapter 10: Statistical Learning Theory and Support Vector Machines (SVM).
 Chapter 11: Democracy in machine learning: how to combine different methods.
 Chapter 12: Topdown clustering: Kmeans.
 Chapter 13: Bottomup (agglomerative) clustering.
You can also download the entire book here.
The latest chapter is Chapter 14: Selforganizing maps.
From the previous chapters, you are now familiar with the basic clustering techniques. Clustering identifies group of similar data, in some cases with a hierarchical structure (groups, then groups containing groups, ...). If an internal representation is available, a group can be represented with a prototype. This chapter deals with prototypes arranged according to a regular gridlike structure and influencing each other if they are neighbors in this grid.
The idea is to cluster data (entities) while at the same time visualizing this clustered structure on a twodimensional map. One wants a visualization that is at least approximately coherent with the clustering This should be puzzling enough to continue reading.
Top Stories Past 30 Days  


