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Auto-Machine-<b>learning</b>-Methods-Sys

  • JILRuntime A general purpose, register based virtual machine (VM) that supports object-oriented feat

    JILRuntime A general purpose, register based virtual machine (VM) that supports object-oriented features, reference counting (auto destruction of data as soon as it is no longer used, no garbage collection), exceptions (handled in C/C++ or virtual machine code) and other debugging features. Objects and functions can be written in virtual machine code, as well as in C or C++, or any other language that can interface to C object code. The VM is written for maximum performance and thus is probably not suitable for embedded systems where a small memory footprint is required. Possible uses of the VM are in game development, scientific research, or to provide a stand-alone, general purpose programming environment.

    標簽: object-oriented JILRuntime register supports

    上傳時間: 2013-12-23

    上傳用戶:cc1015285075

  • by Randal L. Schwartz and Tom Phoenix ISBN 0-596-00132-0 Third Edition, published July 2001. (See

    by Randal L. Schwartz and Tom Phoenix ISBN 0-596-00132-0 Third Edition, published July 2001. (See the catalog page for this book.) the text of Learning Perl, 3rd Edition. Table of Contents Copyright Page Preface Chapter 1: Introduction Chapter 2: Scalar Data Chapter 3: Lists and Arrays Chapter 4: Subroutines Chapter 5: Hashes Chapter 6: I/O Basics Chapter 7: Concepts of Regular Expressions Chapter 8: More About Regular Expressions Chapter 9: Using Regular Expressions Chapter 10: More Control Structures Chapter 11: Filehandles and File Tests Chapter 12: Directory Operations Chapter 13: Manipulating Files and Directories Chapter 14: Process Management Chapter 15: Strings and Sorting Chapter 16: Simple Databases Chapter 17: Some Advanced Perl Techniques Appendix A: Exercise Answers Appendix B: Beyond the Llama Index Colophon

    標簽: L. published Schwartz Edition

    上傳時間: 2014-11-29

    上傳用戶:kr770906

  • by Randal L. Schwartz and Tom Phoenix ISBN 0-596-00132-0 Third Edition, published July 2001. (See

    by Randal L. Schwartz and Tom Phoenix ISBN 0-596-00132-0 Third Edition, published July 2001. (See the catalog page for this book.) Learning Perl, 3rd Edition. Table of Contents Copyright Page Preface Chapter 1: Introduction Chapter 2: Scalar Data Chapter 3: Lists and Arrays Chapter 4: Subroutines Chapter 5: Hashes Chapter 6: I/O Basics Chapter 7: Concepts of Regular Expressions Chapter 8: More About Regular Expressions Chapter 9: Using Regular Expressions Chapter 10: More Control Structures Chapter 11: Filehandles and File Tests Chapter 12: Directory Operations Chapter 13: Manipulating Files and Directories Chapter 14: Process Management Chapter 15: Strings and Sorting Chapter 16: Simple Databases Chapter 17: Some Advanced Perl Techniques Appendix A: Exercise Answers Appendix B: Beyond the Llama Index Colophon

    標簽: L. published Schwartz Edition

    上傳時間: 2015-09-03

    上傳用戶:lifangyuan12

  • acm HDOJ 1051WoodenSticks Description: There is a pile of n wooden sticks. The length and weight o

    acm HDOJ 1051WoodenSticks Description: There is a pile of n wooden sticks. The length and weight of each stick are known in advance. The sticks are to be processed by a woodworking machine in one by one fashion. It needs some time, called setup time, for the machine to prepare processing a stick. The setup times are associated with cleaning operations and changing tools and shapes in the machine. The setup times of the woodworking machine are given as follows: (a) The setup time for the first wooden stick is 1 minute. (b) Right after processing a stick of length l and weight w , the machine will need no setup time for a stick of length l and weight w if l<=l and w<=w . Otherwise, it will need 1 minute for setup.

    標簽: WoodenSticks Description length wooden

    上傳時間: 2014-03-08

    上傳用戶:netwolf

  • Learning Kernel Classifiers: Theory and Algorithms, Introduction This chapter introduces the general

    Learning Kernel Classifiers: Theory and Algorithms, Introduction This chapter introduces the general problem of machine learning and how it relates to statistical inference. 1.1 The Learning Problem and (Statistical) Inference It was only a few years after the introduction of the first computer that one of man’s greatest dreams seemed to be realizable—artificial intelligence. Bearing in mind that in the early days the most powerful computers had much less computational power than a cell phone today, it comes as no surprise that much theoretical research on the potential of machines’ capabilities to learn took place at this time. This becomes a computational problem as soon as the dataset gets larger than a few hundred examples.

    標簽: Introduction Classifiers Algorithms introduces

    上傳時間: 2015-10-20

    上傳用戶:aeiouetla

  • mani: MANIfold learning demonstration GUI by Todd Wittman, Department of Mathematics, University of

    mani: MANIfold learning demonstration GUI by Todd Wittman, Department of Mathematics, University of Minnesota E-mail wittman@math.umn.edu with comments & questions. MANI Website: httP://www.math.umn.edu/~wittman/mani/index.html Last Modified by GUIDE v2.5 10-Apr-2005 13:28:36 Methods obtained from various authors. (1) MDS -- Michael Lee (2) ISOMAP -- J. Tenenbaum, de Silva, & Langford (3) LLE -- Sam Roweis & Lawrence Saul (4) Hessian LLE -- D. Donoho & C. Grimes (5) Laplacian -- M. Belkin & P. Niyogi (6) Diffusion Map -- R. Coifman & S. Lafon (7) LTSA -- Zhenyue Zhang & Hongyuan Zha

    標簽: demonstration Mathematics Department University

    上傳時間: 2016-10-29

    上傳用戶:youmo81

  • A stability analysis is presented for staggered schemes for the governing equations of compressible

    A stability analysis is presented for staggered schemes for the governing equations of compressible flow. The method is based on Fourier analysis. The approximate nature of pressure-correction solution methods is taken into account.  2001 IMACS. Published by Elsevier Science B.V. All rights reserved

    標簽: compressible stability for equations

    上傳時間: 2016-12-02

    上傳用戶:yph853211

  • state of art language modeling methods: An Empirical Study of Smoothing Techniques for Language Mod

    state of art language modeling methods: An Empirical Study of Smoothing Techniques for Language Modeling.pdf BLEU, a Method for Automatic Evaluation of Machine Translation.pdf Class-based n-gram models of natural language.pdf Distributed Language Modeling for N-best List Re-ranking.pdf Distributed Word Clustering for Large Scale Class-Based Language Modeling in.pdf

    標簽: Techniques Empirical Smoothing Language

    上傳時間: 2016-12-26

    上傳用戶:zhuoying119

  • dysii is a C++ library for distributed probabilistic inference and learning in large-scale dynamical

    dysii is a C++ library for distributed probabilistic inference and learning in large-scale dynamical systems. It provides methods such as the Kalman, unscented Kalman, and particle filters and smoothers, as well as useful classes such as common probability distributions and stochastic processes.

    標簽: probabilistic distributed large-scale dynamical

    上傳時間: 2014-01-12

    上傳用戶:wangdean1101

  • Class Wizard to prepare and some commonly used methods, as well as how to use IO to read out the sys

    Class Wizard to prepare and some commonly used methods, as well as how to use IO to read out the system files

    標簽: to commonly prepare methods

    上傳時間: 2017-04-07

    上傳用戶:hullow

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