239 related articles for article (PubMed ID: 11765851)
41. Nonlinear regularization path for quadratic loss support vector machines.
Karasuyama M; Takeuchi I
IEEE Trans Neural Netw; 2011 Oct; 22(10):1613-25. PubMed ID: 21880570
[TBL] [Abstract][Full Text] [Related]
42. A support vector machine approach for detection of microcalcifications.
El-Naqa I; Yang Y; Wernick MN; Galatsanos NP; Nishikawa RM
IEEE Trans Med Imaging; 2002 Dec; 21(12):1552-63. PubMed ID: 12588039
[TBL] [Abstract][Full Text] [Related]
43. [Applications of machine learning in clinical decision support in the omic era].
Zhao XT; Yang YD; Qu HZ; Fang XD
Yi Chuan; 2018 Sep; 40(9):693-703. PubMed ID: 30369474
[TBL] [Abstract][Full Text] [Related]
44. Deep neural mapping support vector machines.
Li Y; Zhang T
Neural Netw; 2017 Sep; 93():185-194. PubMed ID: 28646763
[TBL] [Abstract][Full Text] [Related]
45. Uncertainty quantification and integration of machine learning techniques for predicting acid rock drainage chemistry: a probability bounds approach.
Betrie GD; Sadiq R; Morin KA; Tesfamariam S
Sci Total Environ; 2014 Aug; 490():182-90. PubMed ID: 24852616
[TBL] [Abstract][Full Text] [Related]
46. Comparative study of QSAR/QSPR correlations using support vector machines, radial basis function neural networks, and multiple linear regression.
Yao XJ; Panaye A; Doucet JP; Zhang RS; Chen HF; Liu MC; Hu ZD; Fan BT
J Chem Inf Comput Sci; 2004; 44(4):1257-66. PubMed ID: 15272833
[TBL] [Abstract][Full Text] [Related]
47. Accessing Artificial Intelligence for Fetus Health Status Using Hybrid Deep Learning Algorithm (AlexNet-SVM) on Cardiotocographic Data.
Muhammad Hussain N; Rehman AU; Othman MTB; Zafar J; Zafar H; Hamam H
Sensors (Basel); 2022 Jul; 22(14):. PubMed ID: 35890783
[TBL] [Abstract][Full Text] [Related]
48. Prediction of milk/plasma drug concentration (M/P) ratio using support vector machine (SVM) method.
Zhao C; Zhang H; Zhang X; Zhang R; Luan F; Liu M; Hu Z; Fan B
Pharm Res; 2006 Jan; 23(1):41-8. PubMed ID: 16308669
[TBL] [Abstract][Full Text] [Related]
49. Comparison of medical image classification accuracy among three machine learning methods.
Maruyama T; Hayashi N; Sato Y; Hyuga S; Wakayama Y; Watanabe H; Ogura A; Ogura T
J Xray Sci Technol; 2018; 26(6):885-893. PubMed ID: 30223423
[TBL] [Abstract][Full Text] [Related]
50. A new hybrid intelligent system for accurate detection of Parkinson's disease.
Hariharan M; Polat K; Sindhu R
Comput Methods Programs Biomed; 2014 Mar; 113(3):904-13. PubMed ID: 24485390
[TBL] [Abstract][Full Text] [Related]
51. Data mining and machine learning techniques for the identification of mutagenicity inducing substructures and structure activity relationships of noncongeneric compounds.
Helma C; Cramer T; Kramer S; De Raedt L
J Chem Inf Comput Sci; 2004; 44(4):1402-11. PubMed ID: 15272848
[TBL] [Abstract][Full Text] [Related]
52. CARSVM: a class association rule-based classification framework and its application to gene expression data.
Kianmehr K; Alhajj R
Artif Intell Med; 2008 Sep; 44(1):7-25. PubMed ID: 18586476
[TBL] [Abstract][Full Text] [Related]
53. Classification of faces in man and machine.
Graf AB; Wichmann FA; Bülthoff HH; Schölkopf B
Neural Comput; 2006 Jan; 18(1):143-65. PubMed ID: 16354384
[TBL] [Abstract][Full Text] [Related]
54. Classification of electrocardiogram signals with support vector machines and particle swarm optimization.
Melgani F; Bazi Y
IEEE Trans Inf Technol Biomed; 2008 Sep; 12(5):667-77. PubMed ID: 18779082
[TBL] [Abstract][Full Text] [Related]
55. Regularised extreme learning machine with misclassification cost and rejection cost for gene expression data classification.
Lu H; Wei S; Zhou Z; Miao Y; Lu Y
Int J Data Min Bioinform; 2015; 12(3):294-312. PubMed ID: 26510288
[TBL] [Abstract][Full Text] [Related]
56. Urban Tree Species Classification Using a WorldView-2/3 and LiDAR Data Fusion Approach and Deep Learning.
Hartling S; Sagan V; Sidike P; Maimaitijiang M; Carron J
Sensors (Basel); 2019 Mar; 19(6):. PubMed ID: 30875732
[TBL] [Abstract][Full Text] [Related]
57. New model for prediction binary mixture of antihistamine decongestant using artificial neural networks and least squares support vector machine by spectrophotometry method.
Mofavvaz S; Sohrabi MR; Nezamzadeh-Ejhieh A
Spectrochim Acta A Mol Biomol Spectrosc; 2017 Jul; 182():105-115. PubMed ID: 28412664
[TBL] [Abstract][Full Text] [Related]
58. Extended robust support vector machine based on financial risk minimization.
Takeda A; Fujiwara S; Kanamori T
Neural Comput; 2014 Nov; 26(11):2541-69. PubMed ID: 25058701
[TBL] [Abstract][Full Text] [Related]
59. Machine learning study of classifiers trained with biophysiochemical properties of amino acids to predict fibril forming Peptide motifs.
Kumaran Nair SS; Subba Reddy NV; Hareesha KS
Protein Pept Lett; 2012 Sep; 19(9):917-23. PubMed ID: 22486618
[TBL] [Abstract][Full Text] [Related]
60. Artificial intelligence and machine learning in clinical pharmacological research.
Mayer B; Kringel D; Lötsch J
Expert Rev Clin Pharmacol; 2024 Jan; 17(1):79-91. PubMed ID: 38165148
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]
