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Machine Learning, Clustering, and Polymorphy [article]

Stephen Jose Hanson, Malcolm Bauer
2013 arXiv   pre-print
This paper describes a machine induction program (WITT) that attempts to model human categorization.  ...  This approach represents an alternative to usual Artificial Intelligence approaches to generalization and conceptual clustering which tend to focus on necessary and sufficient feature rules, equivalence  ...  Introduction Most current work done in Artificial Intelligence on machine learning and conceptual clustering--and for that matter most generalization schemes that have bee n proposed in Al-typically rest  ... 
arXiv:1304.3432v1 fatcat:humoceehnbctdgym4za2ha5re4

Conceptual clustering, categorization, and polymorphy

Stephen Jos� Hanson, Malcolm Bauer
1989 Machine Learning  
In this paper we describe WITT, a computational model of categorization and conceptual clustering that has been motivated and guided by research on human categorization.  ...  Properties of categories to which humans are sensitive include best or prototypieal members, relative contrasts between categories, and polymorphy (neither necessary nor sufficient feature rules).  ...  Miller~ and D. Walker for commenting on an earlier version of this paper, and R. A. Amsler for implementing a program that extracted the data on nations from machine-readable text.  ... 
doi:10.1007/bf00116838 fatcat:vhymabvqxvghvcoblg5uggnkfa

Noise and Knowledge Acquisition

Michel Manago, Yves Kodratoff
1987 International Joint Conference on Artificial Intelligence  
In this paper we analyse how noise can affect Knowledge Acquisition from a Machine Learning perspective.  ...  We present some methods to detect and treat noise that goes beyond modulating numerical coefficients and show that noise cannot be viewed as a single entity.  ...  The learning tools used for this research are implemented in COMMON LISP on a TI-EXPLORER Lisp machine and on a SUN 3/160 workstation.  ... 
dblp:conf/ijcai/ManagoK87 fatcat:avr3h6jq3baflisodmkpk7jxk4

Machine-Learned Association of Next-Generation Sequencing-Derived Variants in Thermosensitive Ion Channels Genes with Human Thermal Pain Sensitivity Phenotypes

Jörn Lötsch, Dario Kringel, Gerd Geisslinger, Bruno G. Oertel, Eduard Resch, Sebastian Malkusch
2020 International Journal of Molecular Sciences  
We used machine learning to construct a complex phenotype from pain thresholds to thermal stimuli and associate it with the genetic information derived from the next-generation sequencing (NGS) of 15 ion  ...  After feature selection, 80 genetic variants were retained for an association analysis based on machine learning.  ...  variables for machine learning [78, 79] .  ... 
doi:10.3390/ijms21124367 pmid:32575443 pmcid:PMC7352872 fatcat:ouirg44dvrawrej5dyxxppieym

Mbl-2 gene polymorphisms in pediatric Burkitt lymphoma: an approach based on machine learning techniques

Jonathan Wagner de Medeiros, Anthony José da Cunha Carneiro Lins, Oluwarotimi Williams Samuel, Elker Lene Santos de Lima, Maria Luiza Tabosa de Carvalho Galvão, Bárbara Oliveira Silva, Giwellington Silva Albuquerque, Luísa Priscilla Oliveira de Lima, Maria Tereza Cartaxo Muniz
2021 Research, Society and Development  
Methods: In this article, computational approaches based on the Machine Learning technique were used, where we implemented the Random Forest and KMeans algorithms to classify patterns of individuals diagnosed  ...  the MBL2 gene -221 and -550 regions.  ...  risk factor for BL through using the Machine Learning approach.  ... 
doi:10.33448/rsd-v10i12.20561 fatcat:qgvaf2stnngabbv4suakba3xny

A framework for a theory of automated learning

E. Hausen-Tropper
1996 Theoretical Computer Science  
Topology was chosen as a basis for a theory of learning as it is the study of space invariants which preserve the structure and continuity of a space under "stretchings and deformations".  ...  The choice of topology as a descriptive and predictive theory was inspired by the neurological aspects of synaptic routing under learning, which preserves the original continuity of the routes.  ...  Introduction Machine learning has no fundamental theory outside of Michalski's Conceptual Clustering [ 10, l l] which is based on topological simplexes.  ... 
doi:10.1016/0304-3975(95)00143-3 fatcat:tkoxgez3avhy3onjcssxkjyrbi

EgoNet: identification of human disease ego-network modules

Rendong Yang, Yun Bai, Zhaohui Qin, Tianwei Yu
2014 BMC Genomics  
Results: We present EgoNet, a novel method based on egocentric network-analysis techniques, to exhaustively search and prioritize disease subnetworks and gene markers from a large-scale biological network  ...  When applied to a triple-negative breast cancer (TNBC) microarray dataset, the top selected modules contain both known gene markers in TNBC and novel candidates, such as RAD51 and DOK1, which play a central  ...  Acknowledgements This work was partially supported by NIH grants P20 HL113451, P01 AI096187 and U19 AI057266.  ... 
doi:10.1186/1471-2164-15-314 pmid:24773628 pmcid:PMC4234496 fatcat:syorake5bna6hf3oe7r47yt5qq

APPLICATION OF CLUSTERING TECHNIQUES TO STUDY THE TRAINING PATTERN PROVIDED BY THE DIFFERENT INSTITUTES UNDER HSRT

Ranjan Kumar Gupta, Sudatta Banerjee, Debdip Khan
2020 Zenodo  
mining, Machine Learning, Pattern recognition, Image analysis, Information retrieval and Management.  ...  In this paper an effort has been made to apply common clustering techniques like Hierarchical Agglomerative Clustering and K-mean clustering in analysing the training types and patterns of different training  ...  Gath and Geva (1989) worked on optimal fuzzy clustering and Conceptual clustering, categorization and polymorphy were taken care by Hanson and Bauer (1989) .  ... 
doi:10.5281/zenodo.3953552 fatcat:j4lij6rghvhlxlildgt7p6tefi

Using neural networks to modularize software

Robert W. Schwanke, Stephen Jos� Hanson
1994 Machine Learning  
The tool models modularization as nearest-neighbor clustering and classification, and uses the model to make recommendations for improving modularity by rearranging module membership.  ...  The tool's classifier outperformed other classifiers, both in learning and generalization, on a modest but realistic data set.  ...  This work is closest in spirit to the present work, in that it applied machine learning techniques to real-world data and measures success by realworld standards.  ... 
doi:10.1007/bf00993275 fatcat:hniut2gwcbaiffutmgdd2eb5lm

Web–Based Framework For Breast Cancer Classification

Tomasz Bruździński, Adam Krzyżak, Thomas Fevens, Łukasz Jeleń
2014 Journal of Artificial Intelligence and Soft Computing Research  
For that purpose we have compared 3 segmentation methods: k-means, fuzzy c-means and watershed, and based on these segmentations we have constructed a 25–element feature vector.  ...  The feature vector was introduced as an input to 8 classifiers and their accuracy was checked.  ...  K-means clustering One of the simplest unsupervised learning algorithms that solves clustering problem.  ... 
doi:10.1515/jaiscr-2015-0005 fatcat:2r5zzkndqveidmeagyvenhamdy

Data Mining-Konzepte und graphentheoretische Methoden zur Analyse hypertextueller Daten

Matthias Dehmer
2005 Journal for Language Technology and Computational Linguistics  
Bekannte Kategorisierungsverfahren stammen aus dem Bereich des Machine Learning oder basieren z.B. auf Entscheidungsbäumen. • Die Regressionsanalyse (Hastie et al. 2001 ): Die Regressionsanalyse ist ein  ...  Der Begriff der Polymorphie bezieht sich et al. 1992; Ehud et al. 1994).  ... 
dblp:journals/ldvf/Dehmer05 fatcat:2jxazvd3yrdnviuimchsaf37om

Does Outside-In Teaching Improve the Learning of Object-Oriented Programming?

Erica Janke, Philipp Brune, Stefan Wagner
2015 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering  
We evaluate the initial motivation and knowledge of the participants and the learning outcomes.  ...  Regarding the learning outcomes, the results show no signif- icant differences between the Outside-In and the "traditional" teaching method.  ...  ACKNOWLEDGMENTS The present work as part of the EVELIN project was funded by the German Federal Ministry of Education and Research under grant number 01PL12022E.  ... 
doi:10.1109/icse.2015.173 dblp:conf/icse/JankeBW15 fatcat:tol4c2uisbcezfom7yrjbyea7a

An Android Application Sandbox system for suspicious software detection

Thomas Bläsing, Leonid Batyuk, Aubrey-Derrick Schmidt, Seyit Ahmet Camtepe, Sahin Albayrak
2010 2010 5th International Conference on Malicious and Unwanted Software  
Both the sandbox and the detection algorithms can be deployed in the cloud, providing a fast and distributed detection of suspicious software in a mobile software store akin to Google's Android Market.  ...  This makes it harder to detect and react upon malware attacks if using conventional techniques.  ...  Our aim is to achieve this in future by employing various machine-learning techniques.  ... 
doi:10.1109/malware.2010.5665792 dblp:conf/malware/BlasingBSCA10 fatcat:wt24jaelp5dj3inf2uzykoxyom

Discovery of genomic variations by whole-genome resequencing of the North American Araucana chicken

Rooksana E. Noorai, Vijay Shankar, Nowlan H. Freese, Christopher M. Gregorski, Susan C. Chapman, Peng Xu
2019 PLoS ONE  
, a pea comb, and rumplessness.  ...  The population has maintained variants for clean-faced and tufted, as well as tailed and rumplessness traits making it advantageous for genetic studies.  ...  Acknowledgments The authors would like to acknowledge that Clemson University's Palmetto High-Performance Computing Cluster was used for the analysis of the high-throughput sequence data.  ... 
doi:10.1371/journal.pone.0225834 pmid:31821332 pmcid:PMC6903725 fatcat:3s3yef7thbalpgypqhhabkpasu

A case-based assistant for diagnosis and analysis of dysmorphic syndromes

CD. Evans
1995 Medical Informatics  
case-based learning have much in com m on with another machine learning field, increm ental concept formation.  ...  This type of comparison is sometimes referred to as polymorphy or polythetic matching [28, 29, 18] . Both clumping and polymorphy are characteristics seen in dysmorphology.  ...  The fact is that the overlap of features of the two cases that initially form a group provides the node description, and it is this node description that is updated with regard to later (matching) cases  ... 
doi:10.3109/14639239509025350 pmid:8569305 fatcat:yw6qpzunjrecxfsrrhabvtdoti
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