685 hasil untuk machine learning

Intrusion detection method based on machine learning

TIAN Xin-guang1; GAO Li-zhi2; ZHANG Er-yang1

Tongxin xuebao, Pp 108-114 (2006) · 2006 · Tionghoa · DOAJ Scopus

A new intrusion detection method was presented based on machine learning for intrusion detection systems using shell commands as audit data.In the method,multiple dictionaries of shell command sequences of different lengths were constructed to represent the normal behavior profile of a network…

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Human pol II promoter prediction: time series descriptors and machine learning

Gangal, Rajeev; Sharma, Pankaj

Nucleic Acids Res · 2005 · Inggris · PubMed Central

…from non-promoters and the generalization or predictive ability of the machine-learning algorithms. In this paper we attempt to define a novel approach by using unique descriptors and machine-learning methods for the recognition of eukaryotic polymerase II promoters. In this study, non-linear time…

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Clustering WHO-ART Terms Using Semantic Distance and Machine Learning Algorithms

Iavindrasana, Jimison; Bousquet, Cedric; Degoulet, Patrice dkk.

AMIA Annu Symp Proc · 2006 · Inggris · PubMed Central

…as a terminology service on a J2EE server. Two different unsupervised machine learning algorithms (KMeans, Pvclust) clustered WHO-ART terms according to a semantic distance operator previously described. Pvclust grouped 51% of WHO-ART terms. K-Means grouped 100% of WHO-ART terms but 25% clusters…

Machine learning for an expert system to predict preterm birth risk.

Woolery, L K; Grzymala-Busse, J

J Am Med Inform Assoc · 1994 · Inggris · PubMed Central

…health care system. DESIGN: A knowledge-base development methodology used machine learning, statistical analysis, and validation techniques to analyze three large datasets (18,890 subjects and 214 variables). The dependent (i.e., decision) variable studied was weeks of gestation at delivery, with…

Application of inductive machine learning in data mining

Vladislav Miškovic

Vojnotehnički Glasnik, Vol. 49 No. 4-5 · 2001 · Inggris · DOAJ Scopus

This paper considers the application of inductive machine learning methods in data mining and knowledge discovery in databases tasks. Some characteristics of the author's noncommercial System for Data Mining and Knowledge Discovery are presented as Well as the experimental comparison of its…

Tandem machine learning for the identification of genes regulated by transcription factors

Dinakarpandian, Deendayal; Raheja, Venetia; Mehta, Saumil dkk.

BMC Bioinformatics · 2005 · Inggris · PubMed Central

…sites recognized by the factor. In this study, we have developed a tandem machine learning approach for the identification of regulatory target genes based on these parameters and on the corresponding binding site information contents that measure the affinities of the factor for these cognate…

Machine learning models for lung cancer classification using array comparative genomic hybridization.

Aliferis, C. F.; Hardin, D.; Massion, P. P.

Proc AMIA Symp · 2002 · Inggris · PubMed Central

…in a single experiment. The primary goal of this study was to develop machine learning models that classify non-small Lung Cancers according to histopathology types and to compare several machine learning methods in this learning task. DNA from tumors of 37 patients (21 squamous carcinomas, and 16…

Improving machine learning performance by removing redundant cases in medical data sets.

Ohno-Machado, L.; Fraser, H. S.; Ohrn, A.

Proc AMIA Symp · 1998 · Inggris · PubMed Central

Neural network models and other machine learning methods have successfully been applied to several medical classification problems. These models can be periodically refined and retrained as new cases become available. Since training neural networks by backpropagation is time consuming, it is…

A Genetics-Based Machine Learning Approach to Knowledge Discovery in Clinical Data

Holmes, John H.

Proc AMIA Annu Fall Symp · 1996 · Inggris · PubMed Central

Genomic Computing. Explanatory Analysis of Plant Expression Profiling Data Using Machine Learning

Kell, D. B.; Darby, R. M.; Draper, J.

Plant Physiol · 2001 · Inggris · PubMed Central

Optical Coherence Tomography Machine Learning Classifiers for Glaucoma Detection: A Preliminary Study

Burgansky-Eliash, Zvia; Wollstein, Gadi; Chu, Tianjiao dkk.

Invest Ophthalmol Vis Sci · 2005 · Inggris · PubMed Central

PURPOSE: Machine-learning classifiers are trained computerized systems with the ability to detect the relationship between multiple input parameters and a diagnosis. The present study investigated whether the use of machine-learning classifiers improves optical coherence tomography (OCT) glaucoma…

Machine learning for regulatory analysis and transcription factor target prediction in yeast

Holloway, Dustin T.; Kon, Mark; DeLisi, Charles

Syst Synth Biol · 2006 · Inggris · PubMed Central

…remains largely incomplete. In this paper we develop a supervised learning approach to site identification using support vector machines (SVMs) to combine 26 different data types. A comparison with the standard approach to site identification using position specific scoring matrices (PSSMs) for a…

Homology Induction: the use of machine learning to improve sequence similarity searches

Karwath, Andreas; King, Ross D

BMC Bioinformatics · 2002 · Inggris · PubMed Central

…Homology Induction (HI), a new approach to inferring homology. HI uses machine learning to bootstrap from standard sequence similarity search methods. First a standard method is run, then HI learns rules which are true for sequences of high similarity to the target (assumed homologues) and not true…

Applications of Machine Learning in Cancer Prediction and Prognosis

Joseph A. Cruz; David S. Wishart

Cancer Informatics, Vol. 2 · 2006 · Inggris · DOAJ Scopus

Machine learning is a branch of artificial intelligence that employs a variety of statistical, probabilistic and optimization techniques that allows computers to “learn” from past examples and to detect hard-to-discern patterns from large, noisy or complex data sets. This capability is particularly…

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A scalable machine-learning approach to recognize chemical names within large text databases

Wren, Jonathan D

BMC Bioinformatics · 2006 · Inggris · PubMed Central

MOTIVATION: The use or study of chemical compounds permeates almost every scientific field and in each of them, the amount of textual information is growing rapidly. There is a need to accurately identify chemical names within text for a number of informatics efforts such as database curation…

Machine learning techniques in disease forecasting: a case study on rice blast prediction

Kaundal, Rakesh; Kapoor, Amar S; Raghava, Gajendra PS

BMC Bioinformatics · 2006 · Inggris · PubMed Central

…which further increased to r = 0.77 and %MAE = 36.66 when support vector machine (SVM) based method was used. Similarly, cross-location validation achieved r = 0.48, 0.56 and 0.66 using REG, BPNN and GRNN respectively, with their corresponding %MAE as 77.54, 66.11 and 58.26. The SVM-based method…