Machine Learning: New Applications
http://www.maplesoft.com/applications/category.aspx?cid=215
en-us2017 Maplesoft, A Division of Waterloo Maple Inc.Maplesoft Document SystemSat, 25 Nov 2017 01:55:26 GMTSat, 25 Nov 2017 01:55:26 GMTNew applications in the Machine Learning categoryhttp://www.mapleprimes.com/images/mapleapps.gifMachine Learning: New Applications
http://www.maplesoft.com/applications/category.aspx?cid=215
LibLip - multivariate scattered data interpolation and smoothing
https://www.maplesoft.com/applications/view.aspx?SID=4854&ref=Feed
LibLip is a Maple toolbox, which provides many methods to interpolate scattered data (with or without preprocessing) by using only the data itself and one additional parameter - the Lipschitz constant (which is basically the upper bound on the slope of the function). The Lipschitz constant can be automatically estimated from the data.
LibLip also provides approximation methods using locally Lipschitz functions.
If the data contains noise, it can be smoothened using special techniques which rely on linear programming. Lipschitz constant can
also be estimated from noisy data by using sample splitting and cross-validation.
In addition LibLip also accommodates monotonicity and range constraints. It is useful for approximation of functions that are known to be monotone with respect to all or a subset of variables, as well as monotone only on parts of the domain. Range constraints accommodate non-constant bounds on the values of the data and the interpolant.<img src="/view.aspx?si=4854/image.jpg" alt="LibLip - multivariate scattered data interpolation and smoothing" align="left"/>LibLip is a Maple toolbox, which provides many methods to interpolate scattered data (with or without preprocessing) by using only the data itself and one additional parameter - the Lipschitz constant (which is basically the upper bound on the slope of the function). The Lipschitz constant can be automatically estimated from the data.
LibLip also provides approximation methods using locally Lipschitz functions.
If the data contains noise, it can be smoothened using special techniques which rely on linear programming. Lipschitz constant can
also be estimated from noisy data by using sample splitting and cross-validation.
In addition LibLip also accommodates monotonicity and range constraints. It is useful for approximation of functions that are known to be monotone with respect to all or a subset of variables, as well as monotone only on parts of the domain. Range constraints accommodate non-constant bounds on the values of the data and the interpolant.4854Fri, 29 Dec 2006 00:00:00 ZDr. Gleb BeliakovDr. Gleb BeliakovDirected vs Undirected RootedTrees
https://www.maplesoft.com/applications/view.aspx?SID=4589&ref=Feed
When this maplet is run, it allows the student to examine various aspects of rooted trees. The results from Dijkstra's algorithm for shortest path spanning tree, Floyd's allpairs shortest path algorithm, and Prim's Algorithm may be examined. The maplet opens with a window asking the student to input either a directed or undirected graph. A different Maplet appears depending on the student's response. The maplet was constructed using Maple 9.5<img src="/view.aspx?si=4589/thumb.gif" alt="Directed vs Undirected RootedTrees" align="left"/>When this maplet is run, it allows the student to examine various aspects of rooted trees. The results from Dijkstra's algorithm for shortest path spanning tree, Floyd's allpairs shortest path algorithm, and Prim's Algorithm may be examined. The maplet opens with a window asking the student to input either a directed or undirected graph. A different Maplet appears depending on the student's response. The maplet was constructed using Maple 9.54589Mon, 01 Nov 2004 00:00:00 ZLaurie LaceyLaurie LaceyGraph Analysis Maplet
https://www.maplesoft.com/applications/view.aspx?SID=4587&ref=Feed
Takes a graph (in Maple syntax) and allows the user to compute a variety of graph invariants and polynomials for it.<img src="/view.aspx?si=4587//applications/images/app_image_blank_lg.jpg" alt="Graph Analysis Maplet" align="left"/>Takes a graph (in Maple syntax) and allows the user to compute a variety of graph invariants and polynomials for it.4587Mon, 01 Nov 2004 00:00:00 ZPatti BodkinPatti BodkinIntroduction to Fuzzy Controllers
https://www.maplesoft.com/applications/view.aspx?SID=1398&ref=Feed
This worksheet uses FuzzySets for Maple to demonstrate several examples solving fuzzy logic problems in Maple.<img src="/view.aspx?si=1398/FuzzySets_logo.gif" alt="Introduction to Fuzzy Controllers" align="left"/>This worksheet uses FuzzySets for Maple to demonstrate several examples solving fuzzy logic problems in Maple.1398Mon, 01 Nov 2004 00:00:00 ZDouglas HarderDouglas HarderThe Perceptron and Maple - Artificial Neural Network (ANN)
https://www.maplesoft.com/applications/view.aspx?SID=4229&ref=Feed
The purpose of this application is to use Maple as a mathematical foundation for the development of an Artificial Neural Network (ANN). I recommend that you follow each of the sections even though they are repetitious because they will show you the process of how ANN is built. I have chosen the Preceptron to be used in this application because it was the first ANN to be developed (Caudill & Butler, 1992; Caudill & Butler, 1993; Lau, 1991).
The first part of the application focuses on using basic mathematics. The second part focuses on linear algebra. Thus no matter what your level of mathematical expertise, you will be able to follow this application.<img src="/view.aspx?si=4229//applications/images/app_image_blank_lg.jpg" alt="The Perceptron and Maple - Artificial Neural Network (ANN)" align="left"/>The purpose of this application is to use Maple as a mathematical foundation for the development of an Artificial Neural Network (ANN). I recommend that you follow each of the sections even though they are repetitious because they will show you the process of how ANN is built. I have chosen the Preceptron to be used in this application because it was the first ANN to be developed (Caudill & Butler, 1992; Caudill & Butler, 1993; Lau, 1991).
The first part of the application focuses on using basic mathematics. The second part focuses on linear algebra. Thus no matter what your level of mathematical expertise, you will be able to follow this application.4229Tue, 12 Feb 2002 16:54:45 ZJake TrexelJake TrexelNeural network maplet for learning boolean functions
https://www.maplesoft.com/applications/view.aspx?SID=4211&ref=Feed
This maplet generates artificial neural networks for learning boolean functions. The user enters the number of boolean variables and the number of training trials. The program then generates training-input vectors and trains the network on those vectors as many times as the user had specified.
The Maplet then reports the results of the training and its final outputs in the maplet window.<img src="/view.aspx?si=4211//applications/images/app_image_blank_lg.jpg" alt="Neural network maplet for learning boolean functions" align="left"/>This maplet generates artificial neural networks for learning boolean functions. The user enters the number of boolean variables and the number of training trials. The program then generates training-input vectors and trains the network on those vectors as many times as the user had specified.
The Maplet then reports the results of the training and its final outputs in the maplet window.4211Thu, 24 Jan 2002 11:34:07 ZSylvain MuiseSylvain Muise