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Part II
Part II

... A plane trip involves 3 legs, with 2 stopovers: 1) Due east for 620 km, 2) Southeast (45°) for 440 km, 3) 53° south of west, for 550 km. Calculate the plane’s total displacement. ...
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NeuralNets_ch1-2_intro_Eng

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Determination, Uniformity, and Relevance: Normative

A.I. in Power Systems Alarm Processing
A.I. in Power Systems Alarm Processing

... methods process the non-critical alarms generated by a HVPS. In our novel approach, the edit distance of alarm messages are related to the same device and location; Conditional Random Fields to recognise the named entities (e.g., device name, geographic location, device status, data and time); infor ...
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SYST 201 Systems Modeling I

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Addressing Concept-Evolution in Concept-Drifting Data Streams

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... to account for this the techniques used to generate toolpaths for these parts will also become more complex. Genetic algorithms are a very efficient method of converging on a solution where there are a very large number of possible solutions. There are a number of problems that can arise while devel ...
Transforming Probabilities with Combinational Logic
Transforming Probabilities with Combinational Logic

... c) The integer u is not divisible by 2 and 0.1 < z ≤ 0.2. Let w = 2 − 10z. Then 0 ≤ w < 1 and w = 2 − u · 10−n+1 , having at most (n − 1) digits after the decimal point. Thus, based on the induction hypothesis, we can generate w. It follows that z can be generated as z = (1 − 0.5 × w) × 0.4 × 0.5. 2 ...
Lectures on Artificial Intelligence – CS364 Knowledge Engineering
Lectures on Artificial Intelligence – CS364 Knowledge Engineering

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On the Sample Complexity of Reinforcement Learning with a Generative Model

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High Dimensional Similarity Joins: Algorithms and Performance

... include audio, images and time series, as well as mixtures of these. A useful and increasingly common way of carrying out this analysis is by using characteristics of data items to associate them with points in a multidimensional feature space, so that indexing and query processing can be carried ou ...
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Course title Instructor: , Associate Professor, NYUMC Center for Health Informatics &amp; Bioinformatics
Course title Instructor: , Associate Professor, NYUMC Center for Health Informatics & Bioinformatics

... necessary. The course will provide a broad introduction to basic bioinformatics concepts including data structures, functional motifs and pattern searching, alignment, clustering, evolution and phylogenetics, gene expression, and sequence variation. Weekly readings will include book chapters, key jo ...
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Time-Memory Trade-Off for Lattice Enumeration in a Ball
Time-Memory Trade-Off for Lattice Enumeration in a Ball

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... statistical physics because it provides a fine grained description of the system, and can be used to efficiently compute many properties of interests, such as the partition function and its parameterized version [2, 3]. It can be seen that computing the density of states is computationally intractab ...
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here - Institute of Mathematical Statistics

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Artificial Intelligence (AI) Machine Learning and AI Pattern Recognition
Artificial Intelligence (AI) Machine Learning and AI Pattern Recognition

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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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