title: The WEKA workbench. Online appendix for "Data mining: Practical machine learning tools and techniques"

authors: Frank, Eibe

subjects: computer science

abstract: The WEKA workbench is a collection of machine learning algorithms and data preprocessing tools that includes virtually all the algorithms described in our book. It is designed so that you can quickly try out existing methods on new datasets in flexible ways. It provides extensive support for the whole process of experimental data mining, including preparing the input data, evaluating learning schemes statistically, and visualizing the input data and the result of learning. As well as a wide variety of learning algorithms, it includes a wide range of preprocessing tools. This diverse and comprehensive toolkit is accessed through a common interface so that its users can compare different methods and identify those that are most appropriate for the problem at hand. WEKA was developed at the University of Waikato in New Zealand; the name stands for Waikato Environment for Knowledge Analysis. Outside the university the WEKA, pronounced to rhyme with Mecca, is a flightless bird with an inquisitive nature found only on the islands of New Zealand. The system is written in Java and distributed under the terms of the GNU General Public License. It runs on almost any platform and has been tested under Linux, Windows, and Macintosh operating systems.

text: The WEKA workbench. Online appendix for "Data mining: Practical machine learning tools and techniques" The WEKA workbench is a collection of machine learning algorithms and data preprocessing tools that includes virtually all the algorithms described in our book. It is designed so that you can quickly try out existing methods on new datasets in flexible ways. It provides extensive support for the whole process of experimental data mining, including preparing the input data, evaluating learning schemes statistically, and visualizing the input data and the result of learning. As well as a wide variety of learning algorithms, it includes a wide range of preprocessing tools. This diverse and comprehensive toolkit is accessed through a common interface so that its users can compare different methods and identify those that are most appropriate for the problem at hand. WEKA was developed at the University of Waikato in New Zealand; the name stands for Waikato Environment for Knowledge Analysis. Outside the university the WEKA, pronounced to rhyme with Mecca, is a flightless bird with an inquisitive nature found only on the islands of New Zealand. The system is written in Java and distributed under the terms of the GNU General Public License. It runs on almost any platform and has been tested under Linux, Windows, and Macintosh operating systems.

year: 2016

title: Policy Search Based Relational Reinforcement Learning using the Cross-Entropy Method

authors: Sarjant, Samuel

subjects: relational reinforcement learning

abstract: Relational Reinforcement Learning (RRL) is a subfield of machine learning in which a learning agent seeks to maximise a numerical reward within an environment, represented as collections of objects and relations, by performing actions that interact with the environment. The relational representation allows more dynamic environment states than an attribute-based representation of reinforcement learning, but this flexibility also creates new problems such as a potentially infinite number of states.

This thesis describes an RRL algorithm named Cerrla that creates policies directly from a set of learned relational “condition-action” rules using the Cross-Entropy Method (CEM) to control policy creation. The CEM assigns each rule a sampling probability and gradually modifies these probabilities such that the randomly sampled policies consist of ‘better’ rules, resulting in larger rewards received. Rule creation is guided by an inferred partial model of the environment that defines: the minimal conditions needed to take an action, the possible specialisation conditions per rule, and a set of simplification rules to remove redundant and illegal rule conditions, resulting in compact, efficient, and comprehensible policies.

Cerrla is evaluated on four separate environments, where each environment has several different goals. Results show that compared to existing RRL algorithms, Cerrla is able to learn equal or better behaviour in less time on the standard RRL environment. On other larger, more complex environments, it can learn behaviour that is competitive to specialised approaches. The simplified rules and CEM’s bias towards compact policies result in comprehensive and effective relational policies created in a relatively short amount of time.

text: Policy Search Based Relational Reinforcement Learning using the Cross-Entropy Method Relational Reinforcement Learning (RRL) is a subfield of machine learning in which a learning agent seeks to maximise a numerical reward within an environment, represented as collections of objects and relations, by performing actions that interact with the environment. The relational representation allows more dynamic environment states than an attribute-based representation of reinforcement learning, but this flexibility also creates new problems such as a potentially infinite number of states.

This thesis describes an RRL algorithm named Cerrla that creates policies directly from a set of learned relational “condition-action” rules using the Cross-Entropy Method (CEM) to control policy creation. The CEM assigns each rule a sampling probability and gradually modifies these probabilities such that the randomly sampled policies consist of ‘better’ rules, resulting in larger rewards received. Rule creation is guided by an inferred partial model of the environment that defines: the minimal conditions needed to take an action, the possible specialisation conditions per rule, and a set of simplification rules to remove redundant and illegal rule conditions, resulting in compact, efficient, and comprehensible policies.

Cerrla is evaluated on four separate environments, where each environment has several different goals. Results show that compared to existing RRL algorithms, Cerrla is able to learn equal or better behaviour in less time on the standard RRL environment. On other larger, more complex environments, it can learn behaviour that is competitive to specialised approaches. The simplified rules and CEM’s bias towards compact policies result in comprehensive and effective relational policies created in a relatively short amount of time.

year: 2013

title: Te whanau o te maungarongo hikoi: Maori practice in motion (3 July 2006 to 9 July 2006)

authors: Gilgen, Maynard

subjects: Maori

abstract: What you are about to read is, in the main, a narrative. This narrative is designed to do two things. On one level it simply provides an explanation of how Puti and John Snowden used the funding received for this project. This document is therefore a demonstration of their accountability for the monies received. On another, far deeper, level this document operates to demonstrate to the reader the real power of the traditional concepts of whanaungatanga, whakapapa, tikanga, wairua, and manaakitanga. The adults, youths and elders who embarked on this journey had experiences that are not easily explained in written form. This document must try to achieve this task, and only a narrative that attempts to show these traditional concepts in action can do it.

text: Te whanau o te maungarongo hikoi: Maori practice in motion (3 July 2006 to 9 July 2006) What you are about to read is, in the main, a narrative. This narrative is designed to do two things. On one level it simply provides an explanation of how Puti and John Snowden used the funding received for this project. This document is therefore a demonstration of their accountability for the monies received. On another, far deeper, level this document operates to demonstrate to the reader the real power of the traditional concepts of whanaungatanga, whakapapa, tikanga, wairua, and manaakitanga. The adults, youths and elders who embarked on this journey had experiences that are not easily explained in written form. This document must try to achieve this task, and only a narrative that attempts to show these traditional concepts in action can do it.

year: 2008