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It models burst noise (also called popcorn noise or random telegraph signal). If the two possible values that a random variable can take are c 1 {\displaystyle c_{1}} and c 2 {\displaystyle c_{2}} , then the process can be described by the following master equations :
It is also called random telegraph noise (RTN), popcorn noise, impulse noise, bi-stable noise, or random telegraph signal (RTS) noise. It consists of sudden step-like transitions between two or more discrete voltage or current levels, as high as several hundred microvolts , at random and unpredictable times.
Free and open-source software portal; Shogun is a free, open-source machine learning software library written in C++. It offers numerous algorithms and data structures for machine learning problems. It offers interfaces for Octave, Python, R, Java, Lua, Ruby and C# using SWIG.
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 ...
Conditional random fields (CRFs) are a class of statistical modeling methods often applied in pattern recognition and machine learning and used for structured prediction. Whereas a classifier predicts a label for a single sample without considering "neighbouring" samples, a CRF can take context into account.
In the C Standard Library, signal processing defines how a program handles various signals while it executes. A signal can report some exceptional behavior within the program (such as division by zero ), or a signal can report some asynchronous event outside the program (such as someone striking an interactive attention key on a keyboard).
A random vector (that is, a random variable with values in R n) is said to be a white noise vector or white random vector if its components each have a probability distribution with zero mean and finite variance, [clarification needed] and are statistically independent: that is, their joint probability distribution must be the product of the ...
Caffe (Convolutional Architecture for Fast Feature Embedding) is a deep learning framework, originally developed at University of California, Berkeley. It is open source , under a BSD license . [ 4 ]