685 hasil untuk machine learning

Classification of grapevine cultivars using Kirlian camera and machine learning

Danijel SKOČAJ; Igor KONONENKO; Irma TOMAŽIČ dkk.

Acta Agriculturae Slovenica, Vol. 75 No. 1 · 2000 · Slovenia · DOAJ Scopus

…acquired coronas of the berries with numerical parameters and used machine learning algorithms to classify grapevine cultivars. We tested this method on eight grapevine cultivars, performing different tests. The results show that coronas of grapevine berries contain useful information about…

A Classification-Based Machine Learning Approach for the Analysis of Genome-Wide Expression Data

Lyons-Weiler, James; Patel, Satish; Bhattacharya, Soumyaroop

Genome Res · 2003 · Inggris · PubMed Central

…include hierarchical clustering algorithms, t-, F-, and Z-tests, and machine learning approaches. We describe an approach called the maximum difference subset (MDSS) algorithm that combines classification algorithms, classical statistics, and elements of machine learning and provides a coherent…

Building a protein name dictionary from full text: a machine learning term extraction approach

Shi, Lei; Campagne, Fabien

BMC Bioinformatics · 2005 · Inggris · PubMed Central

BACKGROUND: The majority of information in the biological literature resides in full text articles, instead of abstracts. Yet, abstracts remain the focus of many publicly available literature data mining tools. Most literature mining tools rely on pre-existing lexicons of biological names, often…

Predicting the Subcellular Localization of Human Proteins Using Machine Learning and Exploratory Data Analysis

Acquaah-Mensah, George K.; Leach, Sonia M.; Guda, Chittibabu

Genomics Proteomics Bioinformatics · 2006 · Inggris · PubMed Central

Identifying the subcellular localization of proteins is particularly helpful in the functional annotation of gene products. In this study, we use Machine Learning and Exploratory Data Analysis (EDA) techniques to examine and characterize amino acid sequences of human proteins localized in nine…

Machine learning and word sense disambiguation in the biomedical domain: design and evaluation issues

Xu, Hua; Markatou, Marianthi; Dimova, Rositsa dkk.

BMC Bioinformatics · 2006 · Inggris · PubMed Central

…situations, but the problem is still a challenging one. Supervised WSD machine learning (ML) methods have been applied in the biomedical domain and have shown promising results, but the results typically incorporate a number of confounding factors, and it is problematic to truly understand the…

A machine learning strategy to identify candidate binding sites in human protein-coding sequence

Down, Thomas; Leong, Bernard; Hubbard, Tim JP

BMC Bioinformatics · 2006 · Inggris · PubMed Central

BACKGROUND: The splicing of RNA transcripts is thought to be partly promoted and regulated by sequences embedded within exons. Known sequences include binding sites for SR proteins, which are thought to mediate interactions between splicing factors bound to the 5' and 3' splice sites. It would be…

Review of "Data Mining: Practical Machine Learning Tools and Techniques" by Witten and Frank

Azuaje, Francisco

Biomed Eng Online · 2006 · Inggris · PubMed Central

Matematičko modeliranje induktivnog mašinskog učenja / Mathematical modeling of inductive machine learning

Vladislav Miškovic

Vojnotehnički Glasnik, Vol. 51 No. 1 · 2003 · Inggris · DOAJ Scopus

U radu se razmatra modeliranje induktivnog mašinskog učenja (inductive machine learning). Izloženi su najznačajniji matematički modeli i njihov odnos prema praktično realizovanim algoritmima. Prikazane su osnovne karakteristike najpoznatijih svetskih i jednog sopstvenog sistema induktivnog učenja…

Effectiveness of active forgetting in machine learning applied to financial problems

Hirotaka Nakayama; Kengo Yoshii

Journal of Telecommunications and Information Technology, Iss 3 (2002) · 2002 · Inggris · DOAJ Scopus

…this circumstance, in particular, it has been observed that additional learning plays an effective role. However, since the rule for classification becomes more and more complex with only additional learning, some appropriate forgetting is also necessary. It seems natural that many data are…

Integrating Defeasible Argumentation and Machine Learning Techniques

Gomez, Sergio Alejandro; Chesñevar, Carlos Ivan

arXiv · 2004 · Inggris · arXiv

The field of machine learning (ML) is concerned with the question of how to construct algorithms that automatically improve with experience. In recent years many successful ML applications have been developed, such as datamining programs, information-filtering systems, etc. Although ML algorithms…

Machine Learning of User Profiles: Representational Issues

Bloedorn, Eric; Mani, Inderjeet; MacMillan, T. Richard

arXiv · 1997 · Inggris · arXiv

…importance of a suitable generalization hierarchy and representation for learning profiles which are predictively accurate and comprehensible. In our experiments we evaluated both traditional features based on weighted term vectors as well as subject features corresponding to categories which could…

Automatic Resolution of Ambiguous Terms Based on Machine Learning and Conceptual Relations in the UMLS

Liu, Hongfang; Johnson, Stephen B.; Friedman, Carol

J Am Med Inform Assoc · 2002 · Inggris · PubMed Central

…terms that possess multiple UMLS concepts. In the general English domain, machine-learning techniques have been applied to sense-tagged corpora, in which senses (or concepts) of ambiguous terms have been annotated (mostly manually). Sense disambiguation classifiers are then derived to determine…

Machine learning approaches to supporting the identification of photoreceptor-enriched genes based on expression data

Wang, Haiying; Zheng, Huiru; Simpson, David dkk.

BMC Bioinformatics · 2006 · Inggris · PubMed Central

…expression data relevant to retinal development. This paper assesses a machine learning methodology for supporting the identification of photoreceptor enriched genes based on expression data. RESULTS: Based on the analysis of publicly-available gene expression data from the developing mouse retina…

Machine learning for sub-population assessment: evaluating the C-section rate of different physician practices.

Caruana, Rich; Niculescu, Radu S.; Rao, R. Bharat dkk.

Proc AMIA Symp · 2002 · Inggris · PubMed Central

We apply machine learning to the problem of subpopulation assessment for Caesarian Section. In subpopulation assessment, we are interested in making predictions not for a single patient, but for groups of patients. Typically, in any large population, different subpopulations will have different…

IPS: A System That Uses Machine Learning to Help Locate Patient Records for Clinical Research

Cooper, Gregory F.; Buchanan, Bruce G.; Chapman, Wendy dkk.

Proc AMIA Symp · 2001 · Inggris · PubMed Central

The effect of sample size and disease prevalence on supervised machine learning of narrative data.

McKnight, Lawrence K.; Wilcox, Adam; Hripcsak, George

Proc AMIA Symp · 2002 · Inggris · PubMed Central

This paper examines the independent effects of outcome prevalence and training sample sizes on inductive learning performance. We trained 3 inductive learning algorithms (MC4, IB, and Naïve-Bayes) on 60 simulated datasets of parsed radiology text reports labeled with 6 disease states. Data sets…

Modeling Clinical Judgment and Implicit Guideline Compliance in the Diagnosis of Melanomas Using Machine Learning

Sboner, Andrea; Aliferis, Constantin F.

AMIA Annu Symp Proc · 2005 · Inggris · PubMed Central

We explore several machine learning techniques to model clinical decision making of 6 dermatologists in the clinical task of melanoma diagnosis of 177 pigmented skin lesions (76 malignant, 101 benign). In particular we apply Support Vector Machine (SVM) classifiers to model clinician judgments…

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

Schuetz Erin G; Mehta Saumil; Raheja Venetia dkk.

BMC Bioinformatics, Vol. 6 No. 1 · 2005 · Inggris · DOAJ Scopus

…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…

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

King Ross D; Karwath Andreas

BMC Bioinformatics, Vol. 3 No. 1 · 2002 · Inggris · DOAJ Scopus

…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…

Using Abductive Machine Learning for Online Vibration Monitoring of Turbo Molecular Pumps

R.E. Abdel-Aal; M. Raashid

Shock and Vibration, Vol. 6 No. 5-6 · 1999 · Inggris · DOAJ Scopus

…online monitoring scheme. This paper describes the use of modern machine learning techniques for online monitoring of the pump condition through the measurement and analysis of pump vibrations. Abductive machine learning is used for modeling the pump status as ‘good’ or ‘bad’ using both radial and…