Computational complexity versus accuracy in classification of cortical neural signals

2009 ◽  
Author(s):  
Francesco Tenore ◽  
Vikram Aggarwal ◽  
James R. White ◽  
Marc H. Schieber ◽  
Nitish V. Thakor
Keyword(s):  
Computational Complexity ◽  
Neural Signals

Classification of BMI Control Commands Using Extreme Learning Machine from Spike Trains of Simultaneously Recorded 34 CA1 Single Neural Signals

2008 ◽  
Vol 17 (2) ◽  
pp. 33
Author(s):  
Youngbum Lee ◽  
Hyunjoo Lee ◽  
Yiran Lang ◽  
Jinkwon Kim ◽  
Myoungho Lee ◽  
...  
Keyword(s):  
Extreme Learning Machine ◽  
Spike Trains ◽  
Neural Signals ◽  
Learning Machine

Classification of non-stationary neural signals

1998 ◽  
Vol 84 (1-2) ◽  
pp. 155-166 ◽  
Author(s):  
R.K. Snider ◽  
A.B. Bonds
Keyword(s):  
Neural Signals

scholarly journals Reducing the Deterioration of Sentiment Analysis Results Due to the Time Impact

Information ◽  
2018 ◽  
Vol 9 (8) ◽  
pp. 184 ◽  
Author(s):  
Yuliya Rubtsova
Keyword(s):  
Computational Complexity ◽  
Text Classification ◽  
Feature Space ◽  
Sentiment Classification ◽  
Text Collections ◽  
Word Representation ◽  
F Measure ◽  
Over Time

The research identifies and substantiates the problem of quality deterioration in the sentiment classification of text collections identical in composition and characteristics, but staggered over time. It is shown that the quality of sentiment classification can drop up to 15% in terms of the F-measure over a year and a half. This paper presents three different approaches to improving text classification by sentiment in continuously-updated text collections in Russian: using a weighing scheme with linear computational complexity, adding lexicons of emotional vocabulary to the feature space and distributed word representation. All methods are compared, and it is shown which method is most applicable in certain cases. Experiments comparing the methods on sufficiently representative text collections are described. It is shown that suggested approaches could reduce the deterioration of sentiment classification results for collections staggered over time.

scholarly journals Two-Dimensional Multiply-Accumulator for Classification of Neural Signals

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 19714-19725 ◽  
Author(s):  
Yu-Chieh Chen ◽  
Hsin-Chi Chang ◽  
Hsin Chen
Keyword(s):  
Two Dimensional ◽  
Neural Signals

A Comparison of Techniques for Classification of Multiple Neural Signals

1982 ◽  
Vol BME-29 (12) ◽  
pp. 752-759 ◽  
Author(s):  
Bruce C. Wheeler ◽  
William J. Heetderks
Keyword(s):  
Neural Signals ◽  
Comparison Of Techniques

scholarly journals A complete classification of the complexity of the vertex 3-colourability problem for quadruples of induced 5-vertex prohibitions

2020 ◽  
Vol 22 (1) ◽  
pp. 38-47
Author(s):  
Dmitry S. Malyshev
Keyword(s):  
Graph Theory ◽  
Computational Complexity ◽  
Complete Classification ◽  
Induced Subgraphs ◽  
Simple Graphs ◽  
Forbidden Induced Subgraphs ◽  
The Third ◽  
Hereditary Class ◽  
Complexity Status

The vertex 3-colourability problem for a given graph is to check whether it is possible to split the set of its vertices into three subsets of pairwise non-adjacent vertices or not. A hereditary class of graphs is a set of simple graphs closed under isomorphism and deletion of vertices; the set of its forbidden induced subgraphs defines every such a class. For all but three the quadruples of 5-vertex forbidden induced subgraphs, we know the complexity status of the vertex 3-colourability problem. Additionally, two of these three cases are polynomially equivalent; they also polynomially reduce to the third one. In this paper, we prove that the computational complexity of the considered problem in all of the three mentioned classes is polynomial. This result contributes to the algorithmic graph theory.

scholarly journals Classification of BMI control commands from rat's neural signals using extreme learning machine

2009 ◽  
Vol 8 (1) ◽  
pp. 29 ◽  
Author(s):  
Youngbum Lee ◽  
Hyunjoo Lee ◽  
Jinkwon Kim ◽  
Hyung-Cheul Shin ◽  
Myoungho Lee
Keyword(s):  
Extreme Learning Machine ◽  
Neural Signals ◽  
Learning Machine
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