Batch Input

2003 ◽  
pp. 75-100
Author(s):  
Patrick Theobald
Keyword(s):  
Batch Input

scholarly journals Category Trees - Classifiers that Branch on Category

2021 ◽  
Vol 12 (06) ◽  
pp. 65-76
Author(s):  
Kieran Greer
Keyword(s):  
State Of The Art ◽  
The State ◽  
Biological Analogy ◽  
Category Type ◽  
Benchmark Datasets ◽  
Batch Input ◽  
Distinguishing Features ◽  
Incorrect Data ◽  
Better Than

This paper presents a batch classifier that splits a dataset into tree branches depending on the category type. It has been improved from the earlier version and fixed a mistake in the earlier paper. Two important changes have been made. The first is to represent each category with a separate classifier. Each classifier then classifies its own subset of data rows, using batch input values to create the centroid and also represent the category itself. If the classifier contains data from more than one category however, it needs to create new classifiers for the incorrect data. The second change therefore is to allow the classifier to branch to new layers when there is a split in the data, and create new classifiers there for the data rows that are incorrectly classified. Each layer can therefore branch like a tree - not for distinguishing features, but for distinguishing categories. The paper then suggests a further innovation, which is to represent some data columns with fixed value ranges, or bands. When considering features, it is shown that some of the data can be classified directly through fixed value ranges, while the rest must be classified using a classifier technique and the idea allows the paper to discuss a biological analogy with neurons and neuron links. Tests show that the method can successfully classify a diverse set of benchmark datasets to better than the state-of-the-art.

scholarly journals An LCFS finite buffer model with finite source batch input

1989 ◽  
Vol 26 (02) ◽  
pp. 372-380
Author(s):  
Nico M. Van Dijk
Keyword(s):  
Queueing Systems ◽  
Length Distribution ◽  
Source Distribution ◽  
Closed Form Expression ◽  
Finite Buffer ◽  
Form Expression ◽  
Batch Arrivals ◽  
Queue Length Distribution ◽  
Recursive Computation ◽  
Batch Input

Queueing systems are studied with a last-come, first-served queueing discipline and batch arrivals generated by a finite number of non-exponential sources. A closed-form expression is derived for the steady-state queue length distribution. This expression has a scaled geometric form and is insensitive to the input distribution. Moreover, an algorithm for the recursive computation of the normalizing constant and the busy source distribution is presented. The results are of both practical and theoretical interest as an extension of the standard Poisson batch input case.

scholarly journals An LCFS finite buffer model with finite source batch input

1989 ◽  
Vol 26 (2) ◽  
pp. 372-380 ◽  
Author(s):  
Nico M. Van Dijk
Keyword(s):  
Queueing Systems ◽  
Length Distribution ◽  
Source Distribution ◽  
Closed Form Expression ◽  
Finite Buffer ◽  
Form Expression ◽  
Batch Arrivals ◽  
Queue Length Distribution ◽  
Recursive Computation ◽  
Batch Input

Queueing systems are studied with a last-come, first-served queueing discipline and batch arrivals generated by a finite number of non-exponential sources. A closed-form expression is derived for the steady-state queue length distribution. This expression has a scaled geometric form and is insensitive to the input distribution. Moreover, an algorithm for the recursive computation of the normalizing constant and the busy source distribution is presented. The results are of both practical and theoretical interest as an extension of the standard Poisson batch input case.

Batch-Input

1998 ◽  
pp. 295-304
Author(s):  
Ulrich Mende
Keyword(s):  
Batch Input

Batch Input

2004 ◽  
pp. 77-102
Author(s):  
Patrick Theobald
Keyword(s):  
Batch Input

Batch Input Recording

2001 ◽  
pp. 116-127
Author(s):  
Axel Angeli ◽  
Ulrich Streit ◽  
Robi Gonfalonieri
Keyword(s):  
Batch Input
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