The use of an adaptive threshold element to design a linear optimal pattern classifier

Koford, J. S.; Groner, Gabriel F.

doi:10.1109/tit.1966.1053856

Cited by 103 publications

(21 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Support Vector Machines [5,6] is one such important classification algorithm supported by threshold based decision making. There exists several classification problems that extensively uses thresholding [6][7][8][9][10].…”

Section: Threshold In Algorithmsmentioning

confidence: 99%

Threshnomics: An Introduction to Threshold Logic in Algorithms and Circuits

James¹

2014

J Comput Sci Syst Biol

View full text Add to dashboard Cite

The suffix 'omics' has been added to the names of many fields to denote studies undertaken on a large scale. Threshold science has become so pervasive in everyday life of making decisions. This paper provides a brief introduction and review to the emerging field of Threshnomics by explaining the threshold science applied in various domains. This paper reflects the wide range of threshold applications in algorithms and circuit design and attempt to correlate the threshold notion through neuronal equivalent operator.

show abstract

Section: Threshold In Algorithmsmentioning

confidence: 99%

Threshnomics: An Introduction to Threshold Logic in Algorithms and Circuits

James¹

2014

J Comput Sci Syst Biol

View full text Add to dashboard Cite

show abstract

“…However, we are not aware of any research that has studied the properties of such functions. The first MP-based criterion proposed may be due to Koford and Groner (1966), who proposed a linear classification rule with a fixed value of c, estimating b such that a weighted sum of the external deviations (d,) is minimized. However, Koford and Groner utilized an adaptive algorithm that does not guarantee convergence to the optimal solution.…”

Section: The Choice Of Functional Form Of the Classification Rulementioning

confidence: 99%

“…For the two-group classification problem, some authors have proposed classification models that optimize robust training sample accuracy criteria (Koford and Groner 1966;Ibaraki and Muroga 1970;Liittschwager and Wang 1978;Glover 1981a, 1981b;Bajgier and Hill 1982;Glover et al 1988;Stam and Joachimsthaler 1989;Duarte Silva and Stam 1994a). The classification performance of these methods is mixed, but somewhat promising for non-normal data conditions Stam 1988, 1990).…”

Section: Introductionmentioning

confidence: 99%

Nonparametric Two-Group Classification: Concepts and a SAS-Based Software Package

Silva¹,

Stam²

1998

The American Statistician

View full text Add to dashboard Cite

“…Early efforts in the study of time-domain Adaptive Noise Cancellation (ANC) were made in the 1960s [8,9] and the technique was formalized in 1975 [10]. The method used a minimum of two channels: a reference channel intended to receive only undesired background noise and a primary channel containing the signal plus background noise.…”

Section: Introductionmentioning

confidence: 99%

A Background Noise Reduction Technique using Adaptive Noise Cancellation for Microphone Arrays

Spalt

Fuller

Brooks

et al. 2011

17th AIAA/CEAS Aeroacoustics Conference (32nd AIAA Aeroacoustics Conference)

View full text Add to dashboard Cite

Background noise in wind tunnel environments poses a challenge to acoustic measurements due to possible low or negative Signal to Noise Ratios (SNRs) present in the testing environment. This paper overviews the application of time domain Adaptive Noise Cancellation (ANC) to microphone array signals with an intended application of background noise reduction in wind tunnels. An experiment was conducted to simulate background noise from a wind tunnel circuit measured by an out-of-flow microphone array in the tunnel test section. A reference microphone was used to acquire a background noise signal which interfered with the desired primary noise source signal at the array. The technique's efficacy was investigated using frequency spectra from the array microphones, array beamforming of the point source region, and subsequent deconvolution using the Deconvolution Approach for the Mapping of Acoustic Sources (DAMAS) algorithm. Comparisons were made with the conventional techniques for improving SNR of spectral and Cross-Spectral Matrix subtraction. The method was seen to recover the primary signal level in SNRs as low as -29 dB and outperform the conventional methods. A second processing approach using the center array microphone as the noise reference was investigated for more general applicability of the ANC technique. It outperformed the conventional methods at the -29 dB SNR but yielded less accurate results when coherence over the array dropped. This approach could possibly improve conventional testing methodology but must be investigated further under more realistic testing conditions. = data window weighting constant x n = reference input at step n y n = FIR filter output at step n z n = ANC output at step n = coherence ε n = ANC error at step n μ = LMS algorithm step size τ = time delay τ m = propagation time from grid point to microphone m

show abstract

The use of an adaptive threshold element to design a linear optimal pattern classifier

Cited by 103 publications

References 12 publications

Threshnomics: An Introduction to Threshold Logic in Algorithms and Circuits

Threshnomics: An Introduction to Threshold Logic in Algorithms and Circuits

Nonparametric Two-Group Classification: Concepts and a SAS-Based Software Package

A Background Noise Reduction Technique using Adaptive Noise Cancellation for Microphone Arrays

Contact Info

Product

Resources

About