Monograph records
https://feeds.library.caltech.edu/people/Li-Ling/monograph.rss
A Caltech Library Repository Feedhttp://www.rssboard.org/rss-specificationpython-feedgenenTue, 16 Apr 2024 13:51:11 +0000Perceptron learning with random coordinate descent
https://resolver.caltech.edu/CaltechCSTR:2005.006
Authors: {'items': [{'id': 'Li-Ling', 'name': {'family': 'Li', 'given': 'Ling'}}]}
Year: 2005
DOI: 10.7907/Z9PR7SZQ
A perceptron is a linear threshold classifier that separates examples with a hyperplane. It is perhaps the simplest learning model that is used standalone. In this paper, we propose a family of random coordinate descent algorithms for perceptron learning on binary classification problems. Unlike most perceptron learning algorithms which require smooth cost functions, our algorithms directly minimize the training error, and usually achieve the lowest training error compared with other algorithms. The algorithms are also computational efficient. Such advantages make them favorable for both standalone use and ensemble learning, on problems that are not linearly separable. Experiments show that our algorithms work very well with AdaBoost, and achieve the lowest test errors for half of the datasets.https://authors.library.caltech.edu/records/gnt4y-aqb76Data complexity in machine learning
https://resolver.caltech.edu/CaltechCSTR:2006.004
Authors: {'items': [{'id': 'Li-Ling', 'name': {'family': 'Li', 'given': 'Ling'}}, {'id': 'Abu-Mostafa-Y-S', 'name': {'family': 'Abu-Mostafa', 'given': 'Yaser S.'}}]}
Year: 2006
DOI: 10.7907/Z9319SW2
We investigate the role of data complexity in the context of binary classification problems. The universal data complexity is defined for a data set as the Kolmogorov complexity of the mapping enforced by the data set. It is closely related to several existing principles used in machine learning such as Occam's razor, the minimum description length, and the Bayesian approach. The data complexity can also be defined based on a learning model, which is more realistic for applications. We demonstrate the application of the data complexity in two learning problems, data decomposition and data pruning. In data decomposition, we illustrate that a data set is best approximated by its principal subsets which are Pareto optimal with respect to the complexity and the set size. In data pruning, we show that outliers usually have high complexity contributions, and propose methods for estimating the complexity contribution. Since in practice we have to approximate the ideal data complexity measures, we also discuss the impact of such approximations.https://authors.library.caltech.edu/records/ypj0s-xex59