• Corpus ID: 10785195

Inducing Model Trees for Continuous

  title={Inducing Model Trees for Continuous},
  author={ClassesYong Wang},
Many problems encountered when applying machine learning in practice involve predicting a \class" that takes on a continuous numeric value, yet few machine learning schemes are able to do this. This paper describes a \rational reconstruction" of M5, a method developed by Quinlan (1992) for inducing trees of regression models. In order to accommodate data typically encountered in practice it is necessary to deal eeectively with enumerated attributes and with missing values, and techniques… 

Numerical simulation and novel methodology on resilient modulus for traffic loading on road embankment

ABSTRACT Accurate determination of the resilient modulus (MR) of subbase materials and subgrade soil is a major concern and an essential criterion in the design process of the flexible pavement. The

Predicting solar energetic proton events (E > 10 MeV)

A high level of proton radiation exposure can be dangerous to astronauts, satellite equipment, and air passengers/crew flying along polar routes. The presented solar energetic proton (SEP) event



Programs for Machine Learning

In his new book, C4.5: Programs for Machine Learning, Quinlan has put together a definitive, much needed description of his complete system, including the latest developments, which will be a welcome addition to the library of many researchers and students.

Simplifying decision trees

Many systems have been developed for constructing decision trees from collections of examples. Although the decision trees generated by these methods are accurate and efficient, they often suffer the

C4.5: Programs for Machine Learning

A complete guide to the C4.5 system as implemented in C for the UNIX environment, which starts from simple core learning methods and shows how they can be elaborated and extended to deal with typical problems such as missing data and over hitting.

Combining Instance-Based and Model-Based Learning

Simplifying decision trees Proc Workshop on Knowledge Acquisition for Knowledge-based Systems Learning with continuous classes

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