Extensions to Knowledge Acquisition and Effect of Multimodal Representation in Unsupervised Learning

2009 ◽  
pp. 281-305
Author(s):  
Daswin De Silva ◽  
Damminda Alahakoon ◽  
Shyamali Dharmage
Keyword(s):  
Knowledge Acquisition ◽  
Unsupervised Learning ◽  
Multimodal Representation

The Distinction between Linguistic and Conceptual Semantics in Medical Terminology and its Implication for NLP-Based Knowledge Acquisition

1998 ◽  
Vol 37 (04/05) ◽  
pp. 327-333 ◽  
Author(s):  
F. Buekens ◽  
G. De Moor ◽  
A. Waagmeester ◽  
W. Ceusters
Keyword(s):  
Natural Language ◽  
Knowledge Acquisition ◽  
Natural Language Understanding ◽  
Knowledge Bases ◽  
Linguistic Knowledge ◽  
Medical Terminology ◽  
Language Understanding ◽  
Conceptual Semantics

AbstractNatural language understanding systems have to exploit various kinds of knowledge in order to represent the meaning behind texts. Getting this knowledge in place is often such a huge enterprise that it is tempting to look for systems that can discover such knowledge automatically. We describe how the distinction between conceptual and linguistic semantics may assist in reaching this objective, provided that distinguishing between them is not done too rigorously. We present several examples to support this view and argue that in a multilingual environment, linguistic ontologies should be designed as interfaces between domain conceptualizations and linguistic knowledge bases.

154. Knowledge Acquisition from a Plant'S Physical Examination Databank Via Inductive Learning

2002 ◽  
Author(s):  
S. Lin ◽  
J. Yu ◽  
S. Shieh ◽  
F. Chang ◽  
T. Li
Keyword(s):  
Physical Examination ◽  
Knowledge Acquisition ◽  
Inductive Learning

Classification of Observations through Combination of the Dimension Reduction and the Cluster Analysis

2017 ◽  
Vol 7 (8) ◽  
pp. 30
Author(s):  
Hyeuk Kim
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Unsupervised Learning ◽  
Principal Component ◽  
Component Analysis ◽  
Baseball Players ◽  
Partitioning Around Medoids ◽  
Different Characteristics

Unsupervised learning in machine learning divides data into several groups. The observations in the same group have similar characteristics and the observations in the different groups have the different characteristics. In the paper, we classify data by partitioning around medoids which have some advantages over the k-means clustering. We apply it to baseball players in Korea Baseball League. We also apply the principal component analysis to data and draw the graph using two components for axis. We interpret the meaning of the clustering graphically through the procedure. The combination of the partitioning around medoids and the principal component analysis can be used to any other data and the approach makes us to figure out the characteristics easily.

An Unsupervised Learning Algorithm to Compute Fluid Volumes From NMR T1-T2 Logs in Unconventional Reservoirs

2018 ◽  
Vol 59 ◽  
pp. 617-632 ◽  
Author(s):  
Lalitha Venkataramanan ◽  
◽  
Noyan Evirgen ◽  
David F. Allen ◽  
Albina Mutina ◽  
...  
Keyword(s):  
Unsupervised Learning ◽  
Learning Algorithm ◽  
Unconventional Reservoirs
Sign in / Sign up

Export Citation Format

Share Document