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˜ Lecture X ˜
˜ Lecture X ˜

Self Modifying Cartesian Genetic Programming: Finding algorithms
Self Modifying Cartesian Genetic Programming: Finding algorithms

... that indicate the location in the graph where the node takes its inputs from. However SMCGP also has three real-valued genes which encode parameters that may be required for the function (primarily self modification (SM) functions use these and in many cases they are truncated to integers when neces ...
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solutions - MathsGeeks

... b) This is asking for a comment on skewness. Carry out and and as the data is positively skewed. This can clearly be seen by the the box plot i.e. the line is left of centre. c) The data is positively skewed so most delays are small and there are infrequent longer delays. Most passengers would be re ...
CSCE590/822 Data Mining Principles and Applications
CSCE590/822 Data Mining Principles and Applications

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Domain Knowledge and its Impact on Analytics

... was built and delivered great gains where cost per new customer in the top decile was one fifth of the cost per new customer in the bottom decile. These type of results can be expected in models that are developed for existing customers where we have much more and richer type data. But with acquisit ...
Homework 4: Solutions
Homework 4: Solutions

Some “facts” about software…
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... • Significantly cheaper than RAM since it lacks versatility • How does the data get in there? – Mask programming – data is programmed in at the time of silicon fabrication – PROM – special programming devices allow the user to write data one time – EPROM – data is erased under ultra-violet light or ...
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A Scalable Two-Phase Top-DownSpecialization Approach for Data

Discussion Section 1
Discussion Section 1

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STATION #1: Writing Equations in FUNCTION FORM Example: Write

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... Conclusion: separability in the hidden space is perhaps too much to desire ... inspection of clusters is sufficient for perfect classification; add second Gaussian layer to capture this activity; train second RBF on the data (stacking), reducing number of clusters. ...
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python-example

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review1

... double max = _________; for (int i = 0; i < __________; ++i) if (________________) max = a[i]; 6. A recursive binary search algorithm always reduces the problem size by how much at each recursive call? For example, how many searches are needed if the array size is 128? How likely is it that we will ...
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Lecture Slides - Computer Science@IUPUI

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6.S092: Visual Recognition through Machine Learning Competition

knn - MSU CSE
knn - MSU CSE

< 1 ... 104 105 106 107 108 109 110 111 112 ... 124 >

Corecursion

In computer science, corecursion is a type of operation that is dual to recursion. Whereas recursion works analytically, starting on data further from a base case and breaking it down into smaller data and repeating until one reaches a base case, corecursion works synthetically, starting from a base case and building it up, iteratively producing data further removed from a base case. Put simply, corecursive algorithms use the data that they themselves produce, bit by bit, as they become available, and needed, to produce further bits of data. A similar but distinct concept is generative recursion which may lack a definite ""direction"" inherent in corecursion and recursion. Where recursion allows programs to operate on arbitrarily complex data, so long as they can be reduced to simple data (base cases), corecursion allows programs to produce arbitrarily complex and potentially infinite data structures, such as streams, so long as it can be produced from simple data (base cases). Where recursion may not terminate, never reaching a base state, corecursion starts from a base state, and thus produces subsequent steps deterministically, though it may proceed indefinitely (and thus not terminate under strict evaluation), or it may consume more than it produces and thus become non-productive. Many functions that are traditionally analyzed as recursive can alternatively, and arguably more naturally, be interpreted as corecursive functions that are terminated at a given stage, for example recurrence relations such as the factorial.Corecursion can produce both finite and infinite data structures as result, and may employ self-referential data structures. Corecursion is often used in conjunction with lazy evaluation, to only produce a finite subset of a potentially infinite structure (rather than trying to produce an entire infinite structure at once). Corecursion is a particularly important concept in functional programming, where corecursion and codata allow total languages to work with infinite data structures.
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