1. Estimating Link Signatures with Machine Learning Algorithms

2. Machine Learning Algorithms: A Brief Introduction
Supervised Learning, Unsupervised Learning, Reinforcement
Classification vs Regression Problems

3. Supervised Learning, Unsupervised Learning, and Reinforcement
Supervised: specifically labeled training data
Unsupervised: data not marked, machine learns on the fly
Reinforcement: machine attempts to get the largest reward possible

4. Classification vs Regression
Supervised learning includes two categories of algorithms:
a)Classification: for categorical response values, where the data can be separated into specific “classes”
examples: ham/samp , image of a cat/ not a image of a cat, etc.
b)Regression: for continuous-response values(number)
example: Housing Prices or tomorrow’s temperature,etc.

5. Defining our Problem in the Context of Machine Learning
A Formal Definition of Machine Learning:
A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.
Tom M. Mitchell
Task (T): Estimate link signatures between transmitters and receivers
Experience (E): measurements of link signatures in the given area for other nodes that are not the given transmitter receiver channel
Performance (P): Correctness or successfully guessing the link signature

6. Different Algorithms to Try
Support Vector Machine (SVM) --- very promising!
Regression based (SVR)
Decision Tree / Regression Tree
Random Forest (a cluster of decision trees)

7. Support Vector Machines (SVM)
Advantages
Effective in high dimensional spaces.
Prediction accuracy is generally high
Memory Efficient(support vector, training point)
Disadvantages
Long training time
Difficult to understand the learned function(weights)

8. Decision Tree Algorithms
Advantages
Simple, relatively easy to understand
Data does not need to be linearly separable
Not “black box”
Disadvantages
Tend to overfit to training data
Instability -- if the data is even a little different than expected, you get a different tree

9. Features used in Our Problem
Our assumptions about what can be used to predict the next value
Previous features used in neural network approach
New features we may consider adding?

10. Work for the Upcoming Week
Implement SVR and Decision Tree Algorithms in Matlab
Try to come up with new features to give to the algorithms
Look at more algorithms to potentially use