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Books on the topic 'Machined Rules'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Goldsmith, Becky. Appliqué outside the lines with Piece O'Cake Designs: No rules-no ruler. C&T Pub., 2009.

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2

Association, Canadian Binder Manufacturers'. Constitution, by-laws and rules and regulations of the Canadian Binder Manufacturers' Association for the season of 1886. s.n., 1993.

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3

Saydullaeva, Lyudmila. Performing work by the profession of a worker: tractor driver-machinist of agricultural production. Workshop. INFRA-M Academic Publishing LLC., 2023. http://dx.doi.org/10.12737/1859854.

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In the textbook on the implementation of practical work on PM.04 "Performance of work on one or more professions of workers, positions of employees of the MDK.04.01 — Performance of work in the profession of a worker: tractor driver-machinist of agricultural production" the methods and technologies of performing work in crop production during the cultivation of agricultural crops, the general arrangement of machines and aggregates are considered. The agrotechnical requirements for the performance of mechanized work in crop production, information on the preparation of machines for work and the

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4

Apolloni, Bruno. From Synapses to Rules: Discovering Symbolic Rules from Neural Processed Data. Springer US, 2002.

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5

Ames Research Center. Artificial Intelligence Research Branch., ed. Myths and legends in learning classification rules. National Aeronautics and Space Administration, Ames Research Center, Artificial Intelligence Research Branch, 1990.

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6

Buntine, Wray. Myths and legends in learning classification rules. NASA, Ames Research Center, Research Institute for Advanced Computer Science, 1990.

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7

Schmidt, Kathryn. Rule-breaking quilts. American Quilter's Society, 2010.

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8

Diederich, Joachim, ed. Rule Extraction from Support Vector Machines. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-75390-2.

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9

Joachim, Diederich, ed. Rule extraction from support vector machines. Springer, 2008.

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10

International School on Neural Nets "E.R. Caianiello" Fifth Course: From Synapses to Rules: Discovering Symbolic Rules From Neural Processed Data (2002 Erice, Italy). From synapses to rules: Discovering symbolic rules from neural processed data. Kluwer Academic/Plenum Pub., 2002.

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11

Frost & Wood (Smiths Falls, Ont.). Factory rules governing employees of the Frost & Wood Co. Limited, Smith Falls, Canada. s.n., 1986.

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12

New Zealand. Ministry of Transport. Rules for power lifts not exceeding 750 watts (one horsepower). Ministry of Transport, 1985.

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13

1941-, Gorman Michael, and Joint Steering Committee for the Revision of AACR., eds. Anglo-American cataloguing rules second edition: Chapter 9 : computer files : draft revision. Library Association Publishing, 1987.

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14

Gomis-Bellmunt, Oriol. Design rules for actuators in active mechanical systems. Springer, 2010.

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15

Geyer-Schulz, Andreas. Fuzzy rule-based expert systems and genetic machine learning. Physica-Verlag, 1995.

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16

Taniguchi, Shoichi. A conceptual modeling approach to design of catalogs and cataloging rules. Hituzi Syobo Pub., 2007.

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17

Malaysia. Factories and Machinery Act 1967 (Act 139), regulations & rules: As at 1st February 2011. International Law Book Services, 2011.

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18

W, McAllister E., ed. Pipeline rules of thumb handbook: Quick and accurate solutions to your everyday pipeline problems. 7th ed. Gulf Professional/Elsevier, 2009.

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19

Preston, James F. Air conditioning & refrigeration technician's EPA certification guide: Getting certified, understanding the rules & preparing for EPA inspections. D-Amp Publications, 1994.

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20

Bhutan. Electronic voting machine (EVM) rules and regulations of the Kingdom of Bhutan =: ʼBrug gi tshogs rgyan phrul chas (Ai wi aem) bcaʼ yig daṅ sgrig bźi. Election Commission of Bhutan, 2007.

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21

Malaysia. Factories and Machinery Act 1967 (Act 139) & regulations and rules: As at 30th November 2001. International Law Book Services, 2001.

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22

Malaysia. Factories and Machinery Act 1967 (Act 139) & regulations and rules: As at 20th September 2000. International Law Book Services, 2000.

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23

Malaysia. Factories and Machinery Act 1967 (Act 139) & regulations and rules: As at 15th April 2004. International Law Book Services, 2004.

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24

Diederich, Joachim. Rule Extraction from Support Vector Machines. Springer, 2010.

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25

Loosley, Stephen. Machine Rules: A Political Primer. Melbourne University Publishing, 2015.

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26

Machine Rules: A Political Primer. ReadHowYouWant.com, Limited, 2015.

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27

Machine: Third Rule. Wall to Wall Publishing, 2023.

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28

Wallbaum, W. C. Machine: Third Rule. Wall to Wall Publishing, 2021.

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29

Apolloni, Bruno. From Synapses to Rules. Springer, 2012.

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30

Goguen, J. A. The Rewrite Rule Machine Project. 1989.

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31

Compton, Paul, and Byeong-Ho Kang. Ripple-Down Rules: The Alternative to Machine Learning. Taylor & Francis Group, 2021.

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32

Compton, Paul, and Byeong-Ho Kang. Ripple-Down Rules: The Alternative to Machine Learning. Taylor & Francis Group, 2021.

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33

Compton, Paul, and Byeong-Ho Kang. Ripple-Down Rules: The Alternative to Machine Learning. Taylor & Francis Group, 2021.

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34

Compton, Paul, and Byeong-Ho Kang. Ripple-Down Rules: The Alternative to Machine Learning. Taylor & Francis Group, 2021.

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35

Voutilainen, Atro. Part-of-Speech Tagging. Edited by Ruslan Mitkov. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780199276349.013.0011.

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This article outlines the recently used methods for designing part-of-speech taggers; computer programs for assigning contextually appropriate grammatical descriptors to words in texts. It begins with the description of general architecture and task setting. It gives an overview of the history of tagging and describes the central approaches to tagging. These approaches are: taggers based on handwritten local rules, taggers based on n-grams automatically derived from text corpora, taggers based on hidden Markov models, taggers using automatically generated symbolic language models derived using

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36

Parker, Steve. On the Race Track (Machines Rule). Franklin Watts Ltd, 2008.

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37

Mooney, Raymond J. Machine Learning. Edited by Ruslan Mitkov. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780199276349.013.0020.

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This article introduces the type of symbolic machine learning in which decision trees, rules, or case-based classifiers are induced from supervised training examples. It describes the representation of knowledge assumed by each of these approaches and reviews basic algorithms for inducing such representations from annotated training examples and using the acquired knowledge to classify future instances. Machine learning is the study of computational systems that improve performance on some task with experience. Most machine learning methods concern the task of categorizing examples described b

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38

Farquad, Mohammed. Rule Extraction from Support Vector MacHine. GRIN Verlag GmbH, 2013.

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39

Fürnkranz, Johannes, Dragan Gamberger, and Nada Lavrač. Foundations of Rule Learning. Springer London, Limited, 2012.

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40

Fürnkranz, Johannes. Foundations of Rule Learning. Springer, 2012.

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41

Lavrač, Nada, Johannes Fürnkranz, and Dragan Gamberger. Foundations of Rule Learning. Springer, 2014.

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42

(Editor), Bruno Apolloni, and Franz Kurfess (Editor), eds. From Synapses to Rules: Discovering Symbolic Rules from Neural Processed Data. Springer, 2002.

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43

Karol, Amy. Bend-the-Rules Sewing: The Essential Guide to a Whole New Way to Sew. Potter Craft, 2007.

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44

Unwin, William Cawthorne. Elements of Machine Design: An Introduction to the Principles Which Determine the Arrangement and Proportions of the Parts of Machines, and a Collection of Rules for Machine Design. Creative Media Partners, LLC, 2018.

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45

Anglo-American Cataloguing Rules. Amer Library Assn Editions, 1987.

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46

Gunnell, John. Muscle Car: Mighty Machines That Ruled the Road. Krause Publications, 2006.

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47

Rife, Wava. Rulers Quilting Book : Quilting with Rulers on a Domestic Machine: Quilting Patterns. Independently Published, 2021.

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48

Payne, Kenneth. I, Warbot. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780197611692.001.0001.

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Artificial Intelligence is going to war. Intelligent weapon systems are here today, and many more are on the way tomorrow. Already, they're reshaping conflict—from the chaos of battle, with pilotless drones, robot tanks and uninhabited submersibles, to the headquarters far from the action, where generals and politicians use technology to weigh up what to do. AI changes how we fight, and even how likely it is that we will. In battle, warbots will be faster, more agile and more deadly than today's crewed weapons. New tactics and concepts will emerge, with spoofing and swarming to fool and overwh

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49

Onuf, Nicholas Greenwood. Transitional Figures: J. L. Austin, Jay Forrester, Donna Haraway. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190879808.003.0012.

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Taking the so-called language turn in the 1970s, writers in literature and the arts indiscriminately deployed the adjectives “postmodern” and “postmodernist” to describe what comes next. Linking cognition and rule demands a turn specifically to speech and its function in making social experience intelligible. Speaking is doing; speakers seek to affect listeners, who respond by doing something themselves. Systematizing this simple claim of Austin’s offers a table of speech acts, rules, and rule—a classical response to modernist thinking, and not a transition. Instead the explosion of machine-pr

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50

Liu, Han, Alexander Gegov, and Mihaela Cocea. Rule Based Systems for Big Data: A Machine Learning Approach. Springer International Publishing AG, 2016.

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