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SQL Anywhere® Server - Programming
SQL Anywhere® Server - Programming

Transact-SQL User-Defined Functions
Transact-SQL User-Defined Functions

Using the GNU Compiler Collection
Using the GNU Compiler Collection

Contributions to Deep Learning Models - RiuNet
Contributions to Deep Learning Models - RiuNet

... The goal of this thesis is to present some contributions to the Deep Learning framework, particularly focused on computer vision problems dealing with images. These contributions can be summarized in two novel methods proposed: a new regularization technique for Restricted Boltzmann Machines called ...
PDF Format
PDF Format

... Install MongoDB on Red Hat Enterprise, CentOS, Fedora, or Amazon Linux This tutorial outlines the steps to install MongoDB on Red Hat Enterprise Linux, CentOS Linux, Fedora Linux and related systems. The tutorial uses .rpm packages to install. While some of these distributions include their own Mong ...
Section - GCC, the GNU Compiler Collection
Section - GCC, the GNU Compiler Collection

The Art of Assembly Language - IC
The Art of Assembly Language - IC

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Jess 7.1p2 manual, Microsoft Word format
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Oracle Database Performance Tuning Guide
Oracle Database Performance Tuning Guide

... dangerous applications. It shall be the licensee's responsibility to take all appropriate fail-safe, backup, redundancy and other measures to ensure the safe use of such applications if the Programs are used for such purposes, and we disclaim liability for any damages caused by such use of the Progr ...
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Postgres Plus Enterprise Edition Guide

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Crystal Reports 2008 SP3 User`s Guide
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Crystal Reports 2008 SP1 User`s Guide
Crystal Reports 2008 SP1 User`s Guide

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Cadence PSpice - UNT College of Engineering

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Pattern recognition

Pattern recognition is a branch of machine learning that focuses on the recognition of patterns and regularities in data, although it is in some cases considered to be nearly synonymous with machine learning. Pattern recognition systems are in many cases trained from labeled ""training"" data (supervised learning), but when no labeled data are available other algorithms can be used to discover previously unknown patterns (unsupervised learning).The terms pattern recognition, machine learning, data mining and knowledge discovery in databases (KDD) are hard to separate, as they largely overlap in their scope. Machine learning is the common term for supervised learning methods and originates from artificial intelligence, whereas KDD and data mining have a larger focus on unsupervised methods and stronger connection to business use. Pattern recognition has its origins in engineering, and the term is popular in the context of computer vision: a leading computer vision conference is named Conference on Computer Vision and Pattern Recognition. In pattern recognition, there may be a higher interest to formalize, explain and visualize the pattern, while machine learning traditionally focuses on maximizing the recognition rates. Yet, all of these domains have evolved substantially from their roots in artificial intelligence, engineering and statistics, and they've become increasingly similar by integrating developments and ideas from each other.In machine learning, pattern recognition is the assignment of a label to a given input value. In statistics, discriminant analysis was introduced for this same purpose in 1936. An example of pattern recognition is classification, which attempts to assign each input value to one of a given set of classes (for example, determine whether a given email is ""spam"" or ""non-spam""). However, pattern recognition is a more general problem that encompasses other types of output as well. Other examples are regression, which assigns a real-valued output to each input; sequence labeling, which assigns a class to each member of a sequence of values (for example, part of speech tagging, which assigns a part of speech to each word in an input sentence); and parsing, which assigns a parse tree to an input sentence, describing the syntactic structure of the sentence.Pattern recognition algorithms generally aim to provide a reasonable answer for all possible inputs and to perform ""most likely"" matching of the inputs, taking into account their statistical variation. This is opposed to pattern matching algorithms, which look for exact matches in the input with pre-existing patterns. A common example of a pattern-matching algorithm is regular expression matching, which looks for patterns of a given sort in textual data and is included in the search capabilities of many text editors and word processors. In contrast to pattern recognition, pattern matching is generally not considered a type of machine learning, although pattern-matching algorithms (especially with fairly general, carefully tailored patterns) can sometimes succeed in providing similar-quality output of the sort provided by pattern-recognition algorithms.
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