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Table representation of search results timeline featuring number of search results per year.

Year Number of Results
1950 1
1951 1
1952 1
1956 1
1957 3
1963 5
1964 4
1966 1
1967 1
1978 1
1988 6
1989 97
1990 178
1991 207
1992 221
1993 266
1994 233
1995 251
1996 265
1997 277
1998 309
1999 254
2000 239
2001 221
2002 223
2003 268
2004 275
2005 306
2006 324
2007 366
2008 388
2009 441
2010 518
2011 576
2012 653
2013 695
2014 640
2015 627
2016 614
2017 581
2018 623
2019 401
2020 23
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10,431 results
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Page 1
Introduction to machine learning
Baştanlar Y and Ozuysal M. Methods Mol Biol 2014 - Review. PMID 24272434
[Comparison of Discriminant Analysis and Decision Trees for the Detection of Subclinical Keratoconus].
Kleinhans S, et al. Klin Monbl Augenheilkd 2019. PMID 28810283 German.
PATIENTS AND METHODS: The method of decision tree analysis was compared with discriminant analysis which has shown good results in previous studies. ...RESULTS: Decision trees gave better accuracy and specificity than did discriminant analysis. The sensitivity of decision trees was lower than the sensitivity of discriminant analysis. ...
PATIENTS AND METHODS: The method of decision tree analysis was compared with discriminant analysis which has shown good results in pr …
Lp- and Ls-Norm Distance Based Robust Linear Discriminant Analysis.
Ye Q, et al. Neural Netw 2018. PMID 29940488
Recently, L1-norm distance measure based Linear Discriminant Analysis (LDA) techniques have been shown to be robust against outliers. ...To mitigate this problem, inspired by recent works on Lp-norm based learning, this paper proposes a new discriminant method, called Lp- and Ls-Norm Distance Based Robust Linear Discriminant Analysis (FLDA-Lsp). ...
Recently, L1-norm distance measure based Linear Discriminant Analysis (LDA) techniques have been shown to be robust against outliers. …
Rigorous optimisation of multilinear discriminant analysis with Tucker and PARAFAC structures.
Frølich L, et al. BMC Bioinformatics 2018. PMID 29848301 Free PMC article.
BACKGROUND: We propose rigorously optimised supervised feature extraction methods for multilinear data based on Multilinear Discriminant Analysis (MDA) and demonstrate their usage on Electroencephalography (EEG) and simulated data. ...Our results show that MDA methods applied to raw EEG data can extract discriminatory patterns when compared to traditional unsupervised multilinear feature extraction approaches, whereas the proposed PARAFAC structured MDA models provide meaningful patterns of activity....
BACKGROUND: We propose rigorously optimised supervised feature extraction methods for multilinear data based on Multilinear Discriminant
Folding a small protein using harmonic linear discriminant analysis.
Mendels D, et al. J Chem Phys 2018. PMID 30466286
To address this problem, recently we have introduced Harmonic Linear Discriminant Analysis, a method to systematically construct collective variables as linear combinations of a set of descriptors. ...
To address this problem, recently we have introduced Harmonic Linear Discriminant Analysis, a method to systematically construct coll …
Robust L1-norm two-dimensional linear discriminant analysis.
Li CN, et al. Neural Netw 2015. PMID 25721558
In this paper, we propose an L1-norm two-dimensional linear discriminant analysis (L1-2DLDA) with robust performance. Different from the conventional two-dimensional linear discriminant analysis with L2-norm (L2-2DLDA), where the optimization problem is transferred to a generalized eigenvalue problem, the optimization problem in our L1-2DLDA is solved by a simple justifiable iterative technique, and its convergence is guaranteed. ...
In this paper, we propose an L1-norm two-dimensional linear discriminant analysis (L1-2DLDA) with robust performance. Different from …
Two-dimensional linear discriminant analysis for classification of three-way chemical data.
Silva AC, et al. Anal Chim Acta 2016. PMID 27619086
The two-dimensional linear discriminant analysis (2D-LDA) algorithm was originally proposed in the context of face image processing for the extraction of features with maximal discriminant power. ...The results were compared with those obtained by using the spectral data with no feature extraction, U-PLS-DA (Partial Least Squares Discriminant Analysis applied to the unfolded data), and LDA employing TUCKER-3 or PARAFAC scores. ...
The two-dimensional linear discriminant analysis (2D-LDA) algorithm was originally proposed in the context of face image processing f …
The use of principal component analysis and discriminant analysis in differential sensing routines.
Stewart S, et al. Chem Soc Rev 2014 - Review. PMID 23995750
Statistical analysis techniques such as principal component analysis (PCA) and discriminant analysis (DA) have become an integral part of data analysis for differential sensing. ...Through this paper we hope to present these statistical analysis methods in a manner such that chemists gain further insight into approaches that optimize the discriminatory power of their arrays....
Statistical analysis techniques such as principal component analysis (PCA) and discriminant analysis (DA) have become an integral par …
Robust recursive absolute value inequalities discriminant analysis with sparseness.
Li CN, et al. Neural Netw 2017. PMID 28651080
In this paper, we propose a novel absolute value inequalities discriminant analysis (AVIDA) criterion for supervised dimensionality reduction. Compared with the conventional linear discriminant analysis (LDA), the main characteristics of our AVIDA are robustness and sparseness. ...
In this paper, we propose a novel absolute value inequalities discriminant analysis (AVIDA) criterion for supervised dimensionality r …
Semi-supervised learning for ordinal Kernel Discriminant Analysis.
Pérez-Ortiz M, et al. Neural Netw 2016. PMID 27639724
More specifically, the ordinal version of kernel discriminant learning is extended for this setting considering the neighbourhood information of unlabelled data, which is proposed to be computed in the feature space induced by the kernel function. ...The experiments conducted compare 6 different approaches for semi-supervised learning in the context of ordinal classification in a battery of 30 datasets, showing (1) the good synergy of the ordinal version of discriminant analysis and the use of unlabelled data and (2) the advantage of computing distances in the feature space induced by the kernel function....
More specifically, the ordinal version of kernel discriminant learning is extended for this setting considering the neighbourhood inf …
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