Automatic Classification of Oranges Using Image Processing and Data Mining Techniques
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KNIME Big Data Workshop
KNIME Big Data Workshop Björn Lohrmann KNIME © 2018 KNIME.com AG. All Rights Reserved. Variety, Velocity, Volume • Variety: – Integrating heterogeneous data… – ... and tools • Velocity: – Real time scoring of millions of records/sec – Continuous data streams – Distributed computation • Volume: – From small files... – ...to distributed data repositories – Moving computation to the data © 2018 KNIME.com AG. All Rights Reserved. 2 2 Variety © 2018 KNIME.com AG. All Rights Reserved. 3 The KNIME Analytics Platform: Open for Every Data, Tool, and User Data Scientist Business Analyst KNIME Analytics Platform External Native External Data Data Access, Analysis, and Legacy Connectors Visualization, and Reporting Tools Distributed / Cloud Execution © 2018 KNIME.com AG. All Rights Reserved. 4 Data Integration © 2018 KNIME.com AG. All Rights Reserved. 5 Integrating R and Python © 2018 KNIME.com AG. All Rights Reserved. 6 Modular Integrations © 2018 KNIME.com AG. All Rights Reserved. 7 Other Programming/Scripting Integrations © 2018 KNIME.com AG. All Rights Reserved. 8 Velocity © 2018 KNIME.com AG. All Rights Reserved. 9 Velocity • High Demand Scoring/Prediction: – Scoring using KNIME Server – Scoring with the KNIME Managed Scoring Service • Continuous Data Streams – Streaming in KNIME © 2018 KNIME.com AG. All Rights Reserved. 10 What is High Demand Scoring? • I have a workflow that takes data, applies an algorithm/model and returns a prediction. • I need to deploy that to hundreds or thousands of end users • I need to update the model/workflow periodically © 2018 KNIME.com AG. All Rights Reserved. 11 How to make a scoring workflow • Use JSON Input/Output nodes • Put workflow on KNIME Server -> REST endpoint for scoring © 2018 KNIME.com AG. -
Text Mining Course for KNIME Analytics Platform
Text Mining Course for KNIME Analytics Platform KNIME AG Copyright © 2018 KNIME AG Table of Contents 1. The Open Analytics Platform 2. The Text Processing Extension 3. Importing Text 4. Enrichment 5. Preprocessing 6. Transformation 7. Classification 8. Visualization 9. Clustering 10. Supplementary Workflows Licensed under a Creative Commons Attribution- ® Copyright © 2018 KNIME AG 2 Noncommercial-Share Alike license 1 https://creativecommons.org/licenses/by-nc-sa/4.0/ Overview KNIME Analytics Platform Licensed under a Creative Commons Attribution- ® Copyright © 2018 KNIME AG 3 Noncommercial-Share Alike license 1 https://creativecommons.org/licenses/by-nc-sa/4.0/ What is KNIME Analytics Platform? • A tool for data analysis, manipulation, visualization, and reporting • Based on the graphical programming paradigm • Provides a diverse array of extensions: • Text Mining • Network Mining • Cheminformatics • Many integrations, such as Java, R, Python, Weka, H2O, etc. Licensed under a Creative Commons Attribution- ® Copyright © 2018 KNIME AG 4 Noncommercial-Share Alike license 2 https://creativecommons.org/licenses/by-nc-sa/4.0/ Visual KNIME Workflows NODES perform tasks on data Not Configured Configured Outputs Inputs Executed Status Error Nodes are combined to create WORKFLOWS Licensed under a Creative Commons Attribution- ® Copyright © 2018 KNIME AG 5 Noncommercial-Share Alike license 3 https://creativecommons.org/licenses/by-nc-sa/4.0/ Data Access • Databases • MySQL, MS SQL Server, PostgreSQL • any JDBC (Oracle, DB2, …) • Files • CSV, txt -
Machine Learning Or Econometrics for Credit Scoring: Let’S Get the Best of Both Worlds Elena Dumitrescu, Sullivan Hué, Christophe Hurlin, Sessi Tokpavi
Machine Learning or Econometrics for Credit Scoring: Let’s Get the Best of Both Worlds Elena Dumitrescu, Sullivan Hué, Christophe Hurlin, Sessi Tokpavi To cite this version: Elena Dumitrescu, Sullivan Hué, Christophe Hurlin, Sessi Tokpavi. Machine Learning or Econometrics for Credit Scoring: Let’s Get the Best of Both Worlds. 2020. hal-02507499v2 HAL Id: hal-02507499 https://hal.archives-ouvertes.fr/hal-02507499v2 Preprint submitted on 2 Nov 2020 (v2), last revised 15 Jan 2021 (v3) HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Machine Learning or Econometrics for Credit Scoring: Let's Get the Best of Both Worlds∗ Elena, Dumitrescu,y Sullivan, Hu´e,z Christophe, Hurlin,x Sessi, Tokpavi{ October 18, 2020 Abstract In the context of credit scoring, ensemble methods based on decision trees, such as the random forest method, provide better classification performance than standard logistic regression models. However, logistic regression remains the benchmark in the credit risk industry mainly because the lack of interpretability of ensemble methods is incompatible with the requirements of financial regulators. In this paper, we pro- pose to obtain the best of both worlds by introducing a high-performance and inter- pretable credit scoring method called penalised logistic tree regression (PLTR), which uses information from decision trees to improve the performance of logistic regression. -
Reinforcement Learning in Supervised Problem Domains
Technische Universität München Fakultät für Informatik Lehrstuhl VI – Echtzeitsysteme und Robotik reinforcement learning in supervised problem domains Thomas F. Rückstieß Vollständiger Abdruck der von der Fakultät für Informatik der Technischen Universität München zur Erlangung des akademischen Grades eines Doktors der Naturwissenschaften (Dr. rer. nat.) genehmigten Dissertation. Vorsitzender: Univ.-Prof. Dr. Daniel Cremers Prüfer der Dissertation 1. Univ.-Prof. Dr. Patrick van der Smagt 2. Univ.-Prof. Dr. Hans Jürgen Schmidhuber Die Dissertation wurde am 30. 06. 2015 bei der Technischen Universität München eingereicht und durch die Fakultät für Informatik am 18. 09. 2015 angenommen. Thomas Rückstieß: Reinforcement Learning in Supervised Problem Domains © 2015 email: [email protected] ABSTRACT Despite continuous advances in computing technology, today’s brute for- ce data processing approaches may not provide the necessary advantage to win the race against the ever-growing amount of data that can be wit- nessed over the last decades. In this thesis, we discuss novel methods and algorithms that are capable of directing attention to relevant details and analysing it in sequence to overcome the processing bottleneck and to keep up with this data explosion. In the first of three parts, a novel exploration technique for Policy Gradi- ent Reinforcement Learning is presented which replaces traditional ad- ditive random exploration with state-dependent exploration, exploring on a higher, more strategic level. We will show how this new exploration method converges faster and finds better global solutions than random exploration can. The second part of this thesis will introduce the concept of “data con- sumption” and discuss means to minimise it in supervised learning tasks by deriving classification as a sequential decision process and ma- king it accessible to Reinforcement Learning methods. -
Sage for Lattice-Based Cryptography
Sage for Lattice-based Cryptography Martin R. Albrecht and Léo Ducas Oxford Lattice School Outline Sage Lattices Rings Sage Blurb Sage open-source mathematical software system “Creating a viable free open source alternative to Magma, Maple, Mathematica and Matlab.” Sage is a free open-source mathematics software system licensed under the GPL. It combines the power of many existing open-source packages into a common Python-based interface. How to use it command line run sage local webapp run sage -notebook=jupyter hosted webapp https://cloud.sagemath.com 1 widget http://sagecell.sagemath.org 1On SMC you have the choice between “Sage Worksheet” and “Jupyter Notebook”. We recommend the latter. Python & Cython Sage does not come with yet-another ad-hoc mathematical programming language, it uses Python instead. • one of the most widely used programming languages (Google, IML, NASA, Dropbox), • easy for you to define your own data types and methods on it (bitstreams, lattices, cyclotomic rings, :::), • very clean language that results in easy to read code, • a huge number of libraries: statistics, networking, databases, bioinformatic, physics, video games, 3d graphics, numerical computation (SciPy), and pure mathematic • easy to use existing C/C++ libraries from Python (via Cython) Sage =6 Python Sage Python 1/2 1/2 1/2 0 2^3 2^3 8 1 type(2) type(2) <type 'sage.rings.integer.Integer'> <type 'int'> Sage =6 Python Sage Python type(2r) type(2r) <type 'int'> SyntaxError: invalid syntax type(range(10)[0]) type(range(10)[0]) <type 'int'> <type 'int'> -
User Manual QT
multichannel systems* User Manual QT Information in this document is subject to change without notice. No part of this document may be reproduced or transmitted without the express written permission of Multi Channel Systems MCS GmbH. While every precaution has been taken in the preparation of this document, the publisher and the author assume no responsibility for errors or omissions, or for damages resulting from the use of information contained in this document or from the use of programs and source code that may accompany it. In no event shall the publisher and the author be liable for any loss of profit or any other commercial damage caused or alleged to have been caused directly or indirectly by this document. © 2006-2007 Multi Channel Systems MCS GmbH. All rights reserved. Printed: 2007-01-25 Multi Channel Systems MCS GmbH Aspenhaustraße 21 72770 Reutlingen Germany Fon +49-71 21-90 92 5 - 0 Fax +49-71 21-90 92 5 -11 [email protected] www.multichannelsystems.com Microsoft and Windows are registered trademarks of Microsoft Corporation. Products that are referred to in this document may be either trademarks and/or registered trademarks of their respective holders and should be noted as such. The publisher and the author make no claim to these trademarks. Table Of Contents 1 Introduction 1 1.1 About this Manual 1 2 Important Information and Instructions 3 2.1 Operator's Obligations 3 2.2 Guaranty and Liability 3 2.3 Important Safety Advice 4 2.4 Terms of Use for the program 5 2.5 Limitation of Liability 5 3 First Use 7 3.1 -
A Comparison of Open Source Tools for Data Science
2015 Proceedings of the Conference on Information Systems Applied Research ISSN: 2167-1508 Wilmington, North Carolina USA v8 n3651 __________________________________________________________________________________________________________________________ A Comparison of Open Source Tools for Data Science Hayden Wimmer Department of Information Technology Georgia Southern University Statesboro, GA 30260, USA Loreen M. Powell Department of Information and Technology Management Bloomsburg University Bloomsburg, PA 17815, USA Abstract The next decade of competitive advantage revolves around the ability to make predictions and discover patterns in data. Data science is at the center of this revolution. Data science has been termed the sexiest job of the 21st century. Data science combines data mining, machine learning, and statistical methodologies to extract knowledge and leverage predictions from data. Given the need for data science in organizations, many small or medium organizations are not adequately funded to acquire expensive data science tools. Open source tools may provide the solution to this issue. While studies comparing open source tools for data mining or business intelligence exist, an update on the current state of the art is necessary. This work explores and compares common open source data science tools. Implications include an overview of the state of the art and knowledge for practitioners and academics to select an open source data science tool that suits the requirements of specific data science projects. Keywords: Data Science Tools, Open Source, Business Intelligence, Predictive Analytics, Data Mining. 1. INTRODUCTION Data science is an expensive endeavor. One such example is JMP by SAS. SAS is a primary Data science is an emerging field which provider of data science tools. JMP is one of the intersects data mining, machine learning, more modestly priced tools from SAS with the predictive analytics, statistics, and business price for JMP Pro listed as $14,900. -
A Study of Open Source Data Mining Tools and Its Applications
Research Journal of Applied Sciences, Engineering and Technology 10(10): 1108-1132, 2015 ISSN: 2040-7459; e-ISSN: 2040-7467 © Maxwell Scientific Organization, 2015 Submitted: January 31, 2015 Accepted: March 12, 2015 Published: August 05, 2015 A Study of Open Source Data Mining Tools and its Applications P. Subathra, R. Deepika, K. Yamini, P. Arunprasad and Shriram K. Vasudevan Department of Computer Science and Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham (University), Coimbatore, India Abstract: Data Mining is a technology that is used for the process of analyzing and summarizing useful information from different perspectives of data. The importance of choosing data mining software tools for the developing applications using mining algorithms has led to the analysis of the commercially available open source data mining tools. The study discusses about the following likeness of the data mining tools-KNIME, WEKA, ORANGE, R Tool and Rapid Miner. The Historical Development and state-of-art; (i) The Applications supported, (ii) Data mining algorithms are supported by each tool, (iii) The pre-requisites and the procedures to install a tool, (iv) The input file format supported by each tool. To extract useful information from these data effectively and efficiently, data mining tools are used. The availability of many open source data mining tools, there is an increasing challenge in deciding upon the apace-updated tools for a given application. This study has provided a brief study about the open source knowledge discovery tools with their installation process, algorithms support and input file formats support. Keywords: Data mining, KNIME, ORANGE, Rapid Miner, R-tool, WEKA INTRODUCTION correctness in the classification of the data along with its accuracy. -
Analysis of Earthquake Forecasting in India Using Supervised Machine Learning Classifiers
sustainability Article Analysis of Earthquake Forecasting in India Using Supervised Machine Learning Classifiers Papiya Debnath 1, Pankaj Chittora 2 , Tulika Chakrabarti 3, Prasun Chakrabarti 2,4, Zbigniew Leonowicz 5 , Michal Jasinski 5,* , Radomir Gono 6 and Elzbieta˙ Jasi ´nska 7 1 Department of Basic Science and Humanities, Techno International New Town Rajarhat, Kolkata 700156, India; [email protected] 2 Department of Computer Science and Engineering, Techno India NJR Institute of Technology, Udaipur 313003, Rajasthan, India; [email protected] (P.C.); [email protected] (P.C.) 3 Department of Basic Science, Sir Padampat Singhania University, Udaipur 313601, Rajasthan, India; [email protected] 4 Data Analytics and Artificial Intelligence Laboratory, Engineering-Technology School, Thu Dau Mot University, Thu Dau Mot City 820000, Vietnam 5 Department of Electrical Engineering Fundamentals, Faculty of Electrical Engineering, Wroclaw University of Science and Technology, 50-370 Wroclaw, Poland; [email protected] 6 Department of Electrical Power Engineering, Faculty of Electrical Engineering and Computer Science, VSB—Technical University of Ostrava, 708 00 Ostrava, Czech Republic; [email protected] 7 Faculty of Law, Administration and Economics, University of Wroclaw, 50-145 Wroclaw, Poland; [email protected] * Correspondence: [email protected]; Tel.: +48-713-202-022 Abstract: Earthquakes are one of the most overwhelming types of natural hazards. As a result, successfully handling the situation they create is crucial. Due to earthquakes, many lives can be lost, alongside devastating impacts to the economy. The ability to forecast earthquakes is one of the biggest issues in geoscience. Machine learning technology can play a vital role in the field of Citation: Debnath, P.; Chittora, P.; geoscience for forecasting earthquakes. -
Introduction to Data Mining May 2018
Zupan, Demsar: Introduction to Data Mining May 2018 Introduction to Data Mining Working notes for the hands-on course with Orange Data Mining These notes include Orange Welcome to the course on Introduction to Data Mining! You will workflows and visualizations we see how common data mining tasks can be accomplished without will construct during the course. programming. We will use Orange to construct visual data mining The working notes were workflows. Many similar data mining environments exist, but the prepared by Bla" Zupan and authors of these notes prefer Orange for one simple reason—they Janez Dem#ar with help from the are its authors. For the courses offered by Orange developers, members of the Bioinformatics please visit https://orange.biolab.si/training. Lab in Ljubljana that develop If you haven’t already installed Orange, please download the and maintain Orange. installation package from http://orange.biolab.si. Attribution-NonCommercial-NoDerivs CC BY-NC-ND # University of Ljubljana !1 Zupan, Demsar: Introduction to Data Mining May 2018 Lesson 1: Workflows in Orange Orange workflows consist of components that read, process and visualize data. We call them “widgets.” We place the widgets on a drawing board (the “canvas”). Widgets communicate by sending information along with a communication channel. An output from one widget is used as input to another. A screenshot above shows a We construct workflows by dragging widgets onto the canvas and simple workflow with two connecting them by drawing a line from the transmitting widget to connected widgets and one the receiving widget. The widget’s outputs are on the right and the widget without connections. -
C++ GUI Programming with Qt 4, Second Edition by Jasmin Blanchette; Mark Summerfield
C++ GUI Programming with Qt 4, Second Edition by Jasmin Blanchette; Mark Summerfield Publisher: Prentice Hall Pub Date: February 04, 2008 Print ISBN-10: 0-13-235416-0 Print ISBN-13: 978-0-13-235416-5 eText ISBN-10: 0-13-714397-4 eText ISBN-13: 978-0-13-714397-9 Pages: 752 Table of Contents | Index Overview The Only Official, Best-Practice Guide to Qt 4.3 Programming Using Trolltech's Qt you can build industrial-strength C++ applications that run natively on Windows, Linux/Unix, Mac OS X, and embedded Linux without source code changes. Now, two Trolltech insiders have written a start-to-finish guide to getting outstanding results with the latest version of Qt: Qt 4.3. Packed with realistic examples and in-depth advice, this is the book Trolltech uses to teach Qt to its own new hires. Extensively revised and expanded, it reveals today's best Qt programming patterns for everything from implementing model/view architecture to using Qt 4.3's improved graphics support. You'll find proven solutions for virtually every GUI development task, as well as sophisticated techniques for providing database access, integrating XML, using subclassing, composition, and more. Whether you're new to Qt or upgrading from an older version, this book can help you accomplish everything that Qt 4.3 makes possible. • Completely updated throughout, with significant new coverage of databases, XML, and Qtopia embedded programming • Covers all Qt 4.2/4.3 changes, including Windows Vista support, native CSS support for widget styling, and SVG file generation • Contains -
A Gradient-Based Split Criterion for Highly Accurate and Transparent Model Trees
A Gradient-Based Split Criterion for Highly Accurate and Transparent Model Trees Klaus Broelemann and Gjergji Kasneci SCHUFA Holding AG, Wiesbaden, Germany fKlaus.Broelemann,[email protected] Abstract As a consequence, the choice of a predictive model for a specific task includes a trade-off between simplicity and Machine learning algorithms aim at minimizing the transparency on one side and complexity and higher predic- number of false decisions and increasing the accu- tive power on the other side. If the task is mainly driven by racy of predictions. However, the high predictive the quality of the prediction, complex state-of-the-art models power of advanced algorithms comes at the costs are best suited. Yet, many applications require a certain level of transparency. State-of-the-art methods, such as of transparency. This can be driven by regulatory constraints, neural networks and ensemble methods, result in but also by the user’s wish for transparency. A recent study highly complex models with little transparency. shows that model developers often feel the pressure to use We propose shallow model trees as a way to com- transparent models [Veale et al., 2018], even if more accu- bine simple and highly transparent predictive mod- rate models are available. This leads to simple models, such els for higher predictive power without losing the as linear or logistic regression, where more complex models transparency of the original models. We present would have much higher predictive power. In other cases, a novel split criterion for model trees that allows transparency is not required, but optional.