Support Vector Machines - A New Approach to Learning
Malik Magdon-Ismail, Jennie Yoder, Yaser Abu-Mostafa

Support Vector Machines are a method of extracting information from few noisy data points. A classification boundary is created allowing the largest possible margin of error. The technique is robust and easily implemented. (full report)

Learning in Hardware
Alexander Nicholson, Arrigo Benedetti, Yaser Abu-Mostafa, Pietro Perona

We investigate the use of learning and adaptation for digital hardware design. We use reconfigurable hardware devices and discrete optimization methods to learn circuits from a set of examples. We have shown that this approach works well for the design of small arithmetic circuits and that significant performance improvements may be achieved by moving away from a strictly evolvable (genetic algorithms) approach. (full report)

The Bin Model for Generalization
Alexander Nicholson, Xubo Song, Yaser Abu-Mostafa

The problem of overfitting the data is attacked by using the Bin Model analysis. This provides a method of bounding generalization error without sacrificing valuable training data. (full report)

Minimal Data Set Optimal Classification
James R. Psota, Malik Magdon-Ismail, Yaser Abu-Mostafa

We are developing classification techniques to detect the nature of a pump malfunction given pump vibration sensor data. The size of the data set is very minimal, creating the need for an extremely robust classifier that incorporates all available information. We investigated several generalized nearest neighbor and Bayesian classifiers. By incorporating hints, or information about the problem known independently of the data set, we show that performance can be significantly improved. (full report)

Monotonicity Hints in Machine Learning
Joseph Sill, Yaser Abu-Mostafa

This project focuses on both practical and theoretical aspects of the monotonicity constraint in machine learning. Learning methods which enforce monotonicity in models such as neural networks are being developed. In addition, the flexibility and expressive power of the class of monotonic binary output functions are analyzed and quantified from a theoretical perspective. (full report)