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AP Computer Science Principles
AP Computer Science Principles

What is the computational cost of automating brilliance or serendipity? COS 116: 4/12/11
What is the computational cost of automating brilliance or serendipity? COS 116: 4/12/11

Secrets and Lies, Knowledge and Trust. (Modern cryptography.)
Secrets and Lies, Knowledge and Trust. (Modern cryptography.)

Related Rates
Related Rates

... 5. Write the answer with the correct units. The rate of change of the area of the pizza is at 30π in2/min. Example: A boy launches his toy rocket 15 feet away with his remote control. The toy rocket gains altitude at a rate of 2.5 ft per sec. Find the rate at which the angle of elevation is changing ...
A511 / SMA511 Cascadable Amplifier 10 to 500 MHz
A511 / SMA511 Cascadable Amplifier 10 to 500 MHz

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here. - University of Sussex

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Paper Title - Natural Language Server, Jožef Stefan Institute

... viable and thus more correct. In some cases (HeX) these models were even used to generate a nonsense sentence that sounds right, as a failback method. ...
Comparing Time Series, Neural Nets and
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Optimizing Ecological Sustainability by Integrating Intuition
Optimizing Ecological Sustainability by Integrating Intuition

... Each parameter value is independently drawn from a uniform distribution between 50% and 150% of its default value ...
BMA 140 B01/B02 Statistical Analysis and Business Decision I
BMA 140 B01/B02 Statistical Analysis and Business Decision I

... extremely important to work alone, not in groups. Once you tried to solve the problems, you may wish to compare your solutions with your classmates or request help from the instructor, the Math Help Center, the Business Help Center, or your friends. We will use five teaching materials, in order to i ...
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Expanding small UAV capabilities with ANN : a case - HAL-ENAC

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... number in the sequence is called a term. In this sequence, the first term is 3, the second term is 6, and the third term is 9. When the terms of a sequence change by the same amount each time, the sequence is an arithmetic sequence. ...
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Estimation of prerequisite skills model from large scale assessment

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Improving Learning Performance Through Rational ... Jonathan Gratch*, Steve Chien+, and ...

... To assess these probabilities we must adopt certain statistical assumptions. In this article we adopt the normal parametric model for reasoning about statistical error. This assumes that the difference between the expected utility and estimated utility of a hypothesis can be accurately approximated ...
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A411 / SMA411 Cascadable Amplifier 10 to 400 MHz

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... A events centred on x = 0 B events centred on x = 1 L(f)wrong = Π [f * G(xi,0,σi) + (1-f) * G(xi,1,σi)] L(f)right = Π [f*p(xi,σi;A) + (1-f) * p(xi,σi;B)] ...
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Neural Networks Laboratory EE 329 A Inputs First Hidden layer

Some problems of probabilistic number theory. nogerbek Nurbol 11
Some problems of probabilistic number theory. nogerbek Nurbol 11

Bayesian updating of mechanical models - Application in fracture mechanics
Bayesian updating of mechanical models - Application in fracture mechanics

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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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