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MAX1922 1A Current-Limited Switch for 2 USB Ports General Description Features
MAX1922 1A Current-Limited Switch for 2 USB Ports General Description Features

slides
slides

... the constraint iii can be handled by local time as described in the following result : Let m be a (P, F)-local martingale such that mu ≤ 1 − Zu . Then, mt ≤ (1 − Zt ) on t ∈ [u, ∞) if and only if the local time at zero of m − (1 − Z) on [u, ∞) is identically null. ...
Sets of Boolean Connectives that make Argumentation Easier
Sets of Boolean Connectives that make Argumentation Easier

A Genetic Algorithm Approach to Solve for Multiple Solutions of
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... is called the forward-kinematics (FK) problem. Forwardkinematics of a robot manipulator can easily be solved by knowing the link parameters and joint variables of a robot, while the inverse kinematics is a nonlinear and configuration dependent problem that may have multiple solutions[1]. For some rob ...
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A Formal Characterization of Concept Learning in Description Logics

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Probabilities of hitting a convex hull Linköping University Post Print

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A distributed problem-solving approach to rule induction

... systems. It can be in the form of data exchange, knowledge transfer, or heuristics migration, where the learning mechanisms involved are relatively simple. It can also be done by extending machine learning techniques developed for single-agent systems, such as explanation-based learning, case-based ...
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Near-Optimal Algorithms for Maximum Constraint Satisfaction Problems Moses Charikar Konstantin Makarychev

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... additional operator to talk about the evidence provided by particular observations. We also refine the language to talk about both the prior probability of hypotheses and the posterior probability of hypotheses, taking into account the observation at the states. This lets us write formulas that talk ...
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Best description

inverse probability weighted estimation
inverse probability weighted estimation

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PDF

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Behaviour Analysis of Multilayer Perceptrons with Multiple Hidden

H - Computer Science | SIU
H - Computer Science | SIU

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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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