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Cryptographic hashing - comp
Cryptographic hashing - comp

Lecture 2. Co-Evolution
Lecture 2. Co-Evolution

... competition, it depends only on the number of test cases correctly classified but NOT covered by its opponent ...
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Separating value from choice: delay discounting activity in the lateral

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IOSR Journal of Computer Engineering (IOSR-JCE)

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... This algorithm may be viewed as a combination of DFS and BFS: starting from a given configuration (e.g., the initial state), with a minimal depth bound, we perform a DFS search for the goal state through the graph of game states (in which vertices represent game configurations, and edges— legal move ...
Sequential Plan Recognition
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... solution strategies that students can use to solve problems, and variations within each due to exploratory activities and mistakes carried out by the student. Given a set of actions performed by the student, one hypothesis may relate a given action to the solution of the problem, while another may r ...
Connectionism and Information Processing Abstractions
Connectionism and Information Processing Abstractions

... Functionalism in philosophy, information-processing theories in psychology, and the symbolic paradigm in AI all share these assumptions. Although most of the intuitions that drive this point of view arise from a study of cognitive phenomena, the thesis is often extended to include perception; for e ...
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Efficient construction of reversible jump Markov chain Monte Carlo

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Modified and Ensemble Intelligent Water Drop

... friend Dr. Mohammad Azmi Al-Betar for his help and useful discussion during the course of this research. Indeed, without Allah then my parents’ prayers, I could not have completed this research. My special thanks to my beloved parents for their continued supports, encouragements, and prayers. Thank ...
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... The road to building artificial general intelligence (AGI) [9] is not just very complex but the most complex task computer scientists have tried to solve. While over the last 30+ years a great amount of work has been done, much of that work has been narrow from an application standpoint or has been ...
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IOSR Journal of Computer Engineering (IOSR-JCE)

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Performance Analysis of a Heterogeneous Traffic Scheduler

Bridging the Lexical Chasm: Statistical Approaches to Answer
Bridging the Lexical Chasm: Statistical Approaches to Answer

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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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