Rule Extraction, Fuzzy ARTMAP, and Medical Databases

This paper shows how knowledge, in the form of fuzzy rules, can be derived from a self-organizing supervised learning neural network called fuzzy ARTMAP. Rule extraction proceeds in two stages: pruning removes those recognition nodes whose confidence index falls below a selected threshold; and quantization of continuous learned weights allows the final system state to be translated into a usable set of rules. Simulations on a medical prediction problem, the Pima Indian Diabetes (PID) database, illustrate the method. In the simulations, pruned networks about 1/3 the size of the original actually show improved performance. Quantization yields comprehensible rules with only slight degradation in test set prediction performance.British Petroleum (89-A-1204); Defense Advanced Research Projects Agency (AFOSR-90-0083, ONR-N00014-92-J-4015); National Science Foundation (IRI-90-00530); Office of Naval Research (N00014-91-J-4100); Air Force Office of Scientific Research (90-0083); Institute of Systems Science (National University of Singapore

A Study of Educational Simulations Part I - Engagement and Learning

Interactive computer simulations with complex representations and sophisticated graphics are a relatively new addition to the classroom, and research in this area is limited. We have conducted over 200 individual student interviews during which the students described what they were thinking as they interacted with simulations. These interviews were conducted as part of the research and design of simulations for the Physics Education Technology (PhET) project. PhET is an ongoing project that has developed over 60 simulations for use in teaching physics, chemistry, and physical science. These interviews are a rich source of information about how students interact with computer simulations and what makes an educationally effective simulation. We have observed that simulations can be highly engaging and educationally effective, but only if the student's interaction with the simulation is directed by the student's own questioning. Here we describe our design process, what features are effective for engaging students in educationally productive interactions and the underlying principles which support our empirically developed guidelines. In a companion paper we describe in detail the design features used to create an intuitive simulation for students to use

Learning to Predict the Cosmological Structure Formation

Matter evolved under influence of gravity from minuscule density fluctuations. Non-perturbative structure formed hierarchically over all scales, and developed non-Gaussian features in the Universe, known as the Cosmic Web. To fully understand the structure formation of the Universe is one of the holy grails of modern astrophysics. Astrophysicists survey large volumes of the Universe and employ a large ensemble of computer simulations to compare with the observed data in order to extract the full information of our own Universe. However, to evolve trillions of galaxies over billions of years even with the simplest physics is a daunting task. We build a deep neural network, the Deep Density Displacement Model (hereafter D$^3$M), to predict the non-linear structure formation of the Universe from simple linear perturbation theory. Our extensive analysis, demonstrates that D$^3$M outperforms the second order perturbation theory (hereafter 2LPT), the commonly used fast approximate simulation method, in point-wise comparison, 2-point correlation, and 3-point correlation. We also show that D$^3$M is able to accurately extrapolate far beyond its training data, and predict structure formation for significantly different cosmological parameters. Our study proves, for the first time, that deep learning is a practical and accurate alternative to approximate simulations of the gravitational structure formation of the Universe.Comment: 8 pages, 5 figures, 1 tabl

Bag-of-Features Image Indexing and Classification in Microsoft SQL Server Relational Database

This paper presents a novel relational database architecture aimed to visual objects classification and retrieval. The framework is based on the bag-of-features image representation model combined with the Support Vector Machine classification and is integrated in a Microsoft SQL Server database.Comment: 2015 IEEE 2nd International Conference on Cybernetics (CYBCONF), Gdynia, Poland, 24-26 June 201

Oersted Medal Lecture 2007: Interactive simulations for teaching physics: What works, what doesn't, and why

We give an overview of the Physics Educational Technology (PhET) project to research and develop web-based interactive simulations for teaching and learning physics. The design philosophy, simulation development and testing process, and range of available simulations are described. The highlights of PhET research on simulation design and effectiveness in a variety of educational settings are provided. This work has shown that a well-designed interactive simulation can be an engaging and effective tool for learning physics

Average case analysis of empirical and explanation-based learning algorithms

We present an approach to modeling the average case behavior of learning algorithms. Our motivation is to mathematically model the performance of learning algorithms in order to better understand the nature of their empirical behavior. We are interested in how differences in learning algorithms influence the expected accuracy of the concepts learned.We present the Average Case Learning Model and apply the model to three learning algorithms: a purely empirical algorithm (Bruner's Wholist), an algorithm which prefers analytical (explanation-based) learning over empirical learning (EBL-FIRST-TM) and an algorithm integrating both analytical and empirical learning (lOSC-TM). The Average Case Learning Model is unique in that it is able to accurately predict the expected behavior of learning algorithms. We compare average case analysis to Valiant's Probably Approximately Correct (PAC) learning model