Neurosolutions for Excel

Total Page:16

File Type:pdf, Size:1020Kb

Neurosolutions for Excel NeuroSolutions for Excel Copyright © 2015 by NeuroDimension, Inc.. All Rights Reserved. NeuroSolutions for Excel Table of contents Welcome to NeuroSolutions for Excel .................................................................... 5 Getting Started ................................................................................................... 5 Launching NeuroSolutions for Excel .................................................................. 5 Getting Started in Excel with NeuroSolutions ...................................................... 7 NeuroSolutions Modules in Excel ......................................................................... 13 Express Builder Module .................................................................................. 13 Get Started ............................................................................................... 14 Apply Solution .......................................................................................... 16 Manage Report Sheets ............................................................................... 17 Data Tools Module ........................................................................................ 18 Preprocess Data ........................................................................................ 19 Difference ............................................................................................. 19 Randomize Rows ................................................................................... 20 Sample ................................................................................................ 20 Shift .................................................................................................... 21 Moving Average .................................................................................... 21 Encode Two Class Column ..................................................................... 22 Translate Symbolic Columns ................................................................... 22 Insert Column Labels ............................................................................. 24 Clean Data ............................................................................................ 24 Analyze .................................................................................................... 25 Correlation ........................................................................................... 26 Time Series Plot .................................................................................... 26 XY Scatter Plot ...................................................................................... 26 Histogram ............................................................................................ 26 Summary Statistics ................................................................................ 27 Trend Accuracy ..................................................................................... 28 Tag Data .................................................................................................. 28 Column(s) As Input ............................................................................... 30 Column(s) As Desired ............................................................................ 30 Column(s) As Symbol ............................................................................ 30 Row(s) As Training ............................................................................... 30 Row(s) As Cross Validation .................................................................... 31 Row(s) As Testing ................................................................................. 31 Row(s) As Production ............................................................................ 31 All Columns As Input ............................................................................ 32 All Non-Numeric Columns As Symbol ...................................................... 32 All Rows As Training ............................................................................. 32 Rows By Percentages ............................................................................. 32 Clear Tags ............................................................................................ 33 Clear Column Tag ................................................................................. 33 Clear Symbol Tag .................................................................................. 33 Clear Row Tag ...................................................................................... 34 Clear All Tags ....................................................................................... 34 Select Cross-Section .............................................................................. 34 Refresh Tag Formats ............................................................................. 34 Neural Network Configuration ......................................................................... 35 2 / 77 NeuroSolutions for Excel Classification MLP ...................................................................................... 35 Classification PNN ..................................................................................... 35 Classification TDNN ................................................................................... 35 Regression MLP ......................................................................................... 36 Regression GRNN ...................................................................................... 36 Regression TDNN ...................................................................................... 36 New Custom Network ................................................................................ 36 Open Existing Network .............................................................................. 37 Train Network Module ................................................................................... 38 Train ....................................................................................................... 39 Train N Times ........................................................................................... 41 Vary a Parameter ...................................................................................... 43 Train Genetic ............................................................................................ 45 Advanced Train Network Module ..................................................................... 49 Leave N Out ............................................................................................. 49 Test Network Module ..................................................................................... 52 Test ......................................................................................................... 52 Test Reports ............................................................................................. 53 Sensitivity About the Mean ......................................................................... 56 Apply Production Dataset ............................................................................... 58 Automation .................................................................................................. 58 New Batch ................................................................................................ 58 Batch Manager .......................................................................................... 60 Run Batch ................................................................................................. 61 Create Network... .................................................................................. 61 Train Network... .................................................................................... 62 Test Network... ..................................................................................... 62 Create Data Files... ................................................................................ 63 Miscellaneous ................................................................................................ 63 Data Sheets .............................................................................................. 63 Goto Active Data Sheet .......................................................................... 63 Data Sheets .......................................................................................... 64 Reports .................................................................................................... 64 Goto Active Report ................................................................................ 64 Reports ................................................................................................ 64 Data Files ................................................................................................. 65 All Files ................................................................................................ 66 Training Files ........................................................................................ 66 Cross Validation Files ............................................................................. 66 Testing Files ......................................................................................... 67 Production Input File ............................................................................. 67 View Data File ....................................................................................... 67 Delete
Recommended publications
  • Neural Networks
    AIAI andand thethe NetNet •• Buyer’sBuyer’s GuideGuide 19.1 Where Intelligent Technology Meets the Real World www.pcai.com IntelligentIntelligent Applications Applications TThehe ModernModern RoboticRobotic Also:Also: “Movement”“Movement” Agents, Business Applications, TThehe StateState ofof AIAI Today:Today: TheThe Business Intelligence, WWeb:eb: AI’sAI’s NewNew PlaygroundPlayground Computational Intelligence, Data Analysis & Mining, Robotics:Robotics: RobotsRobots ThatThat Intelligent Applications, Intelligent Tools, MimicMimic AnimalsAnimals Intelligent Tutoring, Intelligent Web Searching, TThehe NationalNational ScienceScience Neural Networks, Foundation:Foundation: EncouragingEncouraging Robotics, tthehe ResearchersResearchers ofof Speech Recognition, TTomorrowomorrow Web Based Expert Systems, Plus:Plus: AIAI andand thethe Net,Net, Training, Bookzone,Bookzone, Buyer’sBuyer’s Guide,Guide, AI Conferences, ProductProduct UUpdatespdates and more! PC AI 2 19.1 Quantities Limited Buy PC AI Back Issues 1995 1999 A Great Resource 9 #1 Intelligent Tools 13 #1 Intelligent Tools & Languages 9 #2 Fuzzy Logic / Neural Networks (Knowledge Verification) for AI Research 9 #3 Object Oriented Development 13 #2 Rule and Object Oriented 9 #4 Knowledge-Based Systems Development (Data Mining) $8.00/Issue - US 9 #5 AI Languages 13 #3 Neural Nets & Fuzzy Logic (For Us and Canadian and 9 #6 Business Applications (Searching) Foreign Postage 13 #4 Knowledge-Based Systems contact PC AI or visit the 1996 (Fuzzy Logic) 10 #1 Intelligent Applications PC AI web site) 13 #5 Data Mining (Simulation and 10 #2 Object Oriented Development Modeling) Order online at 10 #3 Neural Networks / Fuzzy Logic 13 #6 Business Applications www.pcai.com 10 #4 Knowledge-Based Systems (Machine Learning) Total amount enclosed 10 #5 Genetic Algorithm & Modeling 10 #6 Business Applications 2000 $____________.
    [Show full text]
  • Kriging Prediction with Isotropic Matérn Correlations: Robustness
    Journal of Machine Learning Research 21 (2020) 1-38 Submitted 12/19; Revised 7/20; Published 9/20 Kriging Prediction with Isotropic Mat´ernCorrelations: Robustness and Experimental Designs Rui Tuo∗y [email protected] Wm Michael Barnes '64 Department of Industrial and Systems Engineering Texas A&M University College Station, TX 77843, USA Wenjia Wang∗ [email protected] The Hong Kong University of Science and Technology Clear Water Bay, Kowloon, Hong Kong Editor: Philipp Hennig Abstract This work investigates the prediction performance of the kriging predictors. We derive some error bounds for the prediction error in terms of non-asymptotic probability under the uniform metric and Lp metrics when the spectral densities of both the true and the imposed correlation functions decay algebraically. The Mat´ernfamily is a prominent class of correlation functions of this kind. Our analysis shows that, when the smoothness of the imposed correlation function exceeds that of the true correlation function, the prediction error becomes more sensitive to the space-filling property of the design points. In particular, the kriging predictor can still reach the optimal rate of convergence, if the experimental design scheme is quasi-uniform. Lower bounds of the kriging prediction error are also derived under the uniform metric and Lp metrics. An accurate characterization of this error is obtained, when an oversmoothed correlation function and a space-filling design is used. Keywords: Computer Experiments, Uncertainty Quantification, Scattered Data Approx- imation, Space-filling Designs, Bayesian Machine Learning 1. Introduction In contemporary mathematical modeling and data analysis, we often face the challenge of reconstructing smooth functions from scattered observations.
    [Show full text]
  • Artificial Neural Networks
    ARTIFICIAL NEURAL NETWORKS: A REVIEW OF TRAINING TOOLS Darío Baptista, Fernando Morgado-Dias Madeira Interactive Technologies Institute and Centro de Competências de Ciências Exactas e da Engenharia, Universidade da Madeira Campus da Penteada, 9000-039 Funchal, Madeira, Portugal. Tel:+351 291-705150/1, Fax: +351 291-705199 Abstract: Artificial Neural Networks became a common solution for a wide variety of problems in many fields. The most frequent solution for its implementation consists of building and training the Artificial Neural Network within a computer. For implementing a network in an efficient way, the user can access a large choice of software solutions either commercial or prototypes. Choosing the most convenient solution for the application according to the network architecture, training algorithm, operating system and price can be a complex task. This paper helps the Artificial Neural Network user by providing a large list of solution available and explaining their characteristics and terms of use. The paper is confined to reporting the software products that have been developed for Artificial Neural Networks. The features considered important for this kind of software to have in order to accommodate its users effectively are specified. The development of software that implements Artificial Neural is a rapidly growing field driven by strong research interests as well as urgent practical, economical and social needs. Copyright CONTROLO2012 Keywords: Artificial Neural Networks, Training Tools, Training Algorithms, Software. 1. INTRODUCTION commercialization of new ANN tools. With the purpose to inform which tools are available at present Nowadays, in different areas, it is important to and make the choice of which tool to use, this paper analyse nonlinear data to do prediction, classification contains the description of software that has been or to build models.
    [Show full text]
  • On Prediction Properties of Kriging: Uniform Error Bounds and Robustness
    On Prediction Properties of Kriging: Uniform Error Bounds and Robustness Wenjia Wang∗1, Rui Tuoy2 and C. F. Jeff Wuz3 1The Statistical and Applied Mathematical Sciences Institute, Durham, NC 27709, USA 2Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX 77843, USA 3The H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA Abstract Kriging based on Gaussian random fields is widely used in reconstructing unknown functions. The kriging method has pointwise predictive distributions which are com- putationally simple. However, in many applications one would like to predict for a range of untried points simultaneously. In this work we obtain some error bounds for the simple and universal kriging predictor under the uniform metric. It works for a scattered set of input points in an arbitrary dimension, and also covers the case where the covariance function of the Gaussian process is misspecified. These results lead to a better understanding of the rate of convergence of kriging under the Gaussian or the Matérn correlation functions, the relationship between space-filling designs and kriging models, and the robustness of the Matérn correlation functions. Keywords: Gaussian Process modeling; Uniform convergence; Space-filling designs; Radial arXiv:1710.06959v4 [math.ST] 19 Mar 2019 basis functions; Spatial statistics. ∗Wenjia Wang is a postdoctoral fellow in the Statistical and Applied Mathematical Sciences Institute, Durham, NC 27709, USA (Email: [email protected]); yRui Tuo is Assistant Professor in Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX 77843, USA (Email: [email protected]); Tuo’s work is supported by NSF grant DMS 1564438 and NSFC grants 11501551, 11271355 and 11671386.
    [Show full text]
  • Reverse Image Search for Scientific Data Within and Beyond the Visible
    Journal, Vol. XXI, No. 1, 1-5, 2013 Additional note Reverse Image Search for Scientific Data within and beyond the Visible Spectrum Flavio H. D. Araujo1,2,3,4,a, Romuere R. V. Silva1,2,3,4,a, Fatima N. S. Medeiros3,Dilworth D. Parkinson2, Alexander Hexemer2, Claudia M. Carneiro5, Daniela M. Ushizima1,2,* Abstract The explosion in the rate, quality and diversity of image acquisition instruments has propelled the development of expert systems to organize and query image collections more efficiently. Recommendation systems that handle scientific images are rare, particularly if records lack metadata. This paper introduces new strategies to enable fast searches and image ranking from large pictorial datasets with or without labels. The main contribution is the development of pyCBIR, a deep neural network software to search scientific images by content. This tool exploits convolutional layers with locality sensitivity hashing for querying images across domains through a user-friendly interface. Our results report image searches over databases ranging from thousands to millions of samples. We test pyCBIR search capabilities using three convNets against four scientific datasets, including samples from cell microscopy, microtomography, atomic diffraction patterns, and materials photographs to demonstrate 95% accurate recommendations in most cases. Furthermore, all scientific data collections are released. Keywords Reverse image search — Content-based image retrieval — Scientific image recommendation — Convolutional neural network 1University of California, Berkeley, CA, USA 2Lawrence Berkeley National Laboratory, Berkeley, CA, USA 3Federal University of Ceara,´ Fortaleza, CE, Brazil 4Federal University of Piau´ı, Picos, PI, Brazil 5Federal University of Ouro Preto, Ouro Preto, MG, Brazil *Corresponding author: [email protected] aContributed equally to this work.
    [Show full text]
  • Rational Function Approximation
    Rational function approximation Rational function of degree N = n + m is written as p(x) p + p x + + p xn r(x) = = 0 1 ··· n q(x) q + q x + + q xm 0 1 ··· m Now we try to approximate a function f on an interval containing 0 using r(x). WLOG, we set q0 = 1, and will need to determine the N + 1 unknowns p0,..., pn, q1,..., qm. Numerical Analysis I – Xiaojing Ye, Math & Stat, Georgia State University 240 Pad´eapproximation The idea of Pad´eapproximation is to find r(x) such that f (k)(0) = r (k)(0), k = 0, 1,..., N This is an extension of Taylor series but in the rational form. i Denote the Maclaurin series expansion f (x) = i∞=0 ai x . Then i m i n i ∞ a x q x P p x f (x) r(x) = i=0 i i=0 i − i=0 i − q(x) P P P If we want f (k)(0) r (k)(0) = 0 for k = 0,..., N, we need the − numerator to have 0 as a root of multiplicity N + 1. Numerical Analysis I – Xiaojing Ye, Math & Stat, Georgia State University 241 Pad´eapproximation This turns out to be equivalent to k ai qk i = pk , k = 0, 1,..., N − Xi=0 for convenience we used convention p = = p = 0 and n+1 ··· N q = = q = 0. m+1 ··· N From these N + 1 equations, we can determine the N + 1 unknowns: p0, p1,..., pn, q1,..., qm Numerical Analysis I – Xiaojing Ye, Math & Stat, Georgia State University 242 Pad´eapproximation Example x Find the Pad´eapproximation to e− of degree 5 with n = 3 and m = 2.
    [Show full text]
  • Piecewise Linear Approximation of Streaming Time Series Data with Max-Error Guarantees
    Piecewise Linear Approximation of Streaming Time Series Data with Max-error Guarantees Ge Luo Ke Yi Siu-Wing Cheng Zhenguo Li Wei Fan Cheng He Yadong Mu HKUST Huawei Noah’s Ark Lab AT&T Labs Abstract—Given a time series S = ((x1; y1); (x2; y2);::: ) and f(xi) yi " for all i. This is because the `1=`2-error is a prescribed error bound ", the piecewise linear approximation ill-suitedj − forj ≤ online algorithms as it is a sum of errors over (PLA) problem with max-error guarantees is to construct a the entire time series. When the algorithm has no knowledge piecewise linear function f such that jf(xi) − yij ≤ " for all i. In addition, we would like to have an online algorithm that takes about the future, in particular the length of the time series n, the time series as the records arrive in a streaming fashion, and it is impossible to properly allocate the allowed error budget outputs the pieces of f on-the-fly. This problem has applications over time. Another advantage of the ` -error is that it gives wherever time series data is being continuously collected, but us a guarantee on any data record in1 the time series, while the data collection device has limited local buffer space and the ` =` -error only ensures that the “average” error is good communication bandwidth, so that the data has to be compressed 1 2 without a bound on any particular record. Admittedly, the ` - and sent back during the collection process. 1 Prior work addressed two versions of the problem, where error is sensitive to outliers, but one could remove them before either f consists of disjoint segments, or f is required to be feeding the stream to the PLA algorithm, and there is abundant a continuous piecewise linear function.
    [Show full text]
  • The Nnlib2 Library and Nnlib2rcpp R Package for Implementing Neural Networks
    The nnlib2 library and nnlib2Rcpp R package for implementing neural networks Vasilis N Nikolaidis1 1 University of Peloponnese DOI: 10.21105/joss.02876 Software • Review Summary • Repository • Archive Artificial Neural Networks (ANN or NN) are computing models used in various data-driven applications. Such systems typically consist of a large number of processing elements (or Editor: Kakia Chatsiou nodes), usually organized in layers, which exchange data via weighted connections. An ever- increasing number of different neural network models have been proposed and used. Among Reviewers: the several factors differentiating each model are the network topology, the processing and • @schnorr training methods in nodes and connections, and the sequences utilized for transferring data • @MohmedSoudy to, within and from the model etc. The software presented here is a C++ library of classes and templates for implementing neural network components and models and an R package Submitted: 22 October 2020 that allows users to instantiate and use such components from the R programming language. Published: 23 May 2021 License Authors of papers retain copyright and release the work Statement of need under a Creative Commons Attribution 4.0 International A significant number of capable, flexible, high performance tools for NN are available today, License (CC BY 4.0). including frameworks such as Tensorflow (Abadi et al., 2016) and Torch (Collobert et al., 2011), and related high level APIs including Keras (Chollet & others, 2015) and PyTorch (Paszke et al., 2019). Ready-to-use NN models are also provided by various machine learning platforms such as H2O (H2O.ai, 2020) or libraries, SNNS (Zell et al., 1994) and FANN (Nissen, 2003).
    [Show full text]
  • Neural Network FAQ, Part 1 of 7
    Neural Network FAQ, part 1 of 7: Introduction Archive-name: ai-faq/neural-nets/part1 Last-modified: 2002-05-17 URL: ftp://ftp.sas.com/pub/neural/FAQ.html Maintainer: [email protected] (Warren S. Sarle) Copyright 1997, 1998, 1999, 2000, 2001, 2002 by Warren S. Sarle, Cary, NC, USA. --------------------------------------------------------------- Additions, corrections, or improvements are always welcome. Anybody who is willing to contribute any information, please email me; if it is relevant, I will incorporate it. The monthly posting departs around the 28th of every month. --------------------------------------------------------------- This is the first of seven parts of a monthly posting to the Usenet newsgroup comp.ai.neural-nets (as well as comp.answers and news.answers, where it should be findable at any time). Its purpose is to provide basic information for individuals who are new to the field of neural networks or who are just beginning to read this group. It will help to avoid lengthy discussion of questions that often arise for beginners. SO, PLEASE, SEARCH THIS POSTING FIRST IF YOU HAVE A QUESTION and DON'T POST ANSWERS TO FAQs: POINT THE ASKER TO THIS POSTING The latest version of the FAQ is available as a hypertext document, readable by any WWW (World Wide Web) browser such as Netscape, under the URL: ftp://ftp.sas.com/pub/neural/FAQ.html. If you are reading the version of the FAQ posted in comp.ai.neural-nets, be sure to view it with a monospace font such as Courier. If you view it with a proportional font, tables and formulas will be mangled.
    [Show full text]
  • Rethinking Statistical Learning Theory: Learning Using Statistical Invariants
    Machine Learning (2019) 108:381–423 https://doi.org/10.1007/s10994-018-5742-0 Rethinking statistical learning theory: learning using statistical invariants Vladimir Vapnik1,2 · Rauf Izmailov3 Received: 2 April 2018 / Accepted: 25 June 2018 / Published online: 18 July 2018 © The Author(s) 2018 Abstract This paper introduces a new learning paradigm, called Learning Using Statistical Invariants (LUSI), which is different from the classical one. In a classical paradigm, the learning machine constructs a classification rule that minimizes the probability of expected error; it is data- driven model of learning. In the LUSI paradigm, in order to construct the desired classification function, a learning machine computes statistical invariants that are specific for the problem, and then minimizes the expected error in a way that preserves these invariants; it is thus both data- and invariant-driven learning. From a mathematical point of view, methods of the classical paradigm employ mechanisms of strong convergence of approximations to the desired function, whereas methods of the new paradigm employ both strong and weak convergence mechanisms. This can significantly increase the rate of convergence. Keywords Intelligent teacher · Privileged information · Support vector machine · Neural network · Classification · Learning theory · Regression · Conditional probability · Kernel function · Ill-Posed problem · Reproducing Kernel Hilbert space · Weak convergence Mathematics Subject Classification 68Q32 · 68T05 · 68T30 · 83C32 1 Introduction It is known that Teacher–Student interactions play an important role in human learning. An old Japanese proverb says “Better than thousand days of diligent study is one day with a great teacher.” What is it exactly that great Teachers do? This question remains unanswered.
    [Show full text]
  • From Digital Computing to Brain-Like Neuromorphic Accelerator
    Mahyar SHAHSAVARI Lille University of Science and Technology Doctoral Thesis Unconventional Computing Using Memristive Nanodevices: From Digital Computing to Brain-like Neuromorphic Accelerator Supervisor: Pierre BOULET Thèse en vue de l’obtention du titre de Docteur en INFORMATIQUE DE L’UNIVERSITÉ DE LILLE École Doctorale des Sciences Pour l’Ingénieur de Lille - Nord De France 14-Dec-2016 Jury: Hélène Paugam-Moisy Professor of University of the French West Indies (Université des Antilles), France (Rapporteur) Michel Paindavoine Professor of University of Burgundy (Université de Bourgogne), France (Rapporteur) Virginie Hoel Professor of Lille University of Science and Technology, France(Président) Said Hamdioui Professor of Delft University of Technology, The Netherlands Pierre Boulet Professor of Lille University of Science and Technology, France Acknowledgment Firstly, I would like to express my sincere gratitude to my advisor Prof. Pierre Boulet for all kind of supports during my PhD. I never forget his supports specially during the last step of my PhD that I was very busy due to teaching duties. Pierre was really punctual and during three years of different meetings and discussions, he was always available on time and as a record he never canceled any meeting. I learned a lot form you Pierre, specially the way of writing, communicating and presenting. you were an ideal supervisor for me. Secondly, I am grateful of my co-supervisor Dr Philippe Devienne. As I did not knew any French at the beginning of my study, it was not that easy to settle down and with Philippe supports the life was more comfortable and enjoyable for me and my family here in Lille.
    [Show full text]
  • Function Approximation with Mlps, Radial Basis Functions, and Support Vector Machines
    Table of Contents CHAPTER V- FUNCTION APPROXIMATION WITH MLPS, RADIAL BASIS FUNCTIONS, AND SUPPORT VECTOR MACHINES ..........................................................................................................................................3 1. INTRODUCTION................................................................................................................................4 2. FUNCTION APPROXIMATION ...........................................................................................................7 3. CHOICES FOR THE ELEMENTARY FUNCTIONS...................................................................................12 4. PROBABILISTIC INTERPRETATION OF THE MAPPINGS-NONLINEAR REGRESSION .................................23 5. TRAINING NEURAL NETWORKS FOR FUNCTION APPROXIMATION ......................................................24 6. HOW TO SELECT THE NUMBER OF BASES ........................................................................................28 7. APPLICATIONS OF RADIAL BASIS FUNCTIONS................................................................................38 8. SUPPORT VECTOR MACHINES........................................................................................................42 9. PROJECT: APPLICATIONS OF NEURAL NETWORKS AS FUNCTION APPROXIMATORS ...........................52 10. CONCLUSION ..............................................................................................................................59 CALCULATION OF THE ORTHONORMAL WEIGHTS ..................................................................................63
    [Show full text]