Fusion of supervised and unsupervised learning for improved classification of hyperspectral images

2012 ◽  
Vol 217 ◽  
pp. 39-55 ◽  
Author(s):  
Naif Alajlan ◽  
Yakoub Bazi ◽  
Farid Melgani ◽  
Ronald R. Yager
Keyword(s):  
Unsupervised Learning ◽  
Hyperspectral Images ◽  
Supervised And Unsupervised Learning

Duality Between Learning Machines: A Bridge Between Supervised and Unsupervised Learning

1994 ◽  
Vol 6 (3) ◽  
pp. 491-508 ◽  
Author(s):  
J.-P. Nadal ◽  
N. Parga
Keyword(s):  
Theoretical Analysis ◽  
Unsupervised Learning ◽  
Supervised Learning ◽  
Learning Task ◽  
Maximum Information ◽  
Learning Machines ◽  
Learning Tasks ◽  
Supervised And Unsupervised Learning ◽  
Second Dual

We exhibit a duality between two perceptrons that allows us to compare the theoretical analysis of supervised and unsupervised learning tasks. The first perceptron has one output and is asked to learn a classification of p patterns. The second (dual) perceptron has p outputs and is asked to transmit as much information as possible on a distribution of inputs. We show in particular that the maximum information that can be stored in the couplings for the supervised learning task is equal to the maximum information that can be transmitted by the dual perceptron.

scholarly journals Using clustering to aid text classification of single-labelled datasets

2009 ◽  
Author(s):  
Αντωνία Κυριακοπούλου
Keyword(s):  
Unsupervised Learning ◽  
Text Classification ◽  
Data Representation ◽  
Classification Performance ◽  
Critical Research ◽  
Social Bookmarking ◽  
Supervised And Unsupervised Learning ◽  
Text Classifiers ◽  
Concise Representation

Supervised and unsupervised learning have been the focus of critical research in the areas of machine learning and artificial intelligence. In the literature, these two streams flow independently of each other, despite their close conceptual and practical connections. This dissertation demonstrates that unsupervised learning algorithms, i.e. clustering, can provide us with valuable information about the data and help in the creation of high-accuracy text classifiers. In the case of clustering,the aim is to extract a kind of \structure" from a given sample of objects. The reasoning behind this is that if some structure exists in the objects, it is possible to take advantage of this information and find a short description of the data,exploiting the dependence or association between index terms and documents.This concise representation of the whole dataset can be properly incorporated in the existing data representation. The use of prior knowledge about the nature oft he dataset helps in building a more efficient classifier for this set. This approach does not capture all the intricacies of text; however on some domains this technique substantially improves text classification accuracy.In this vein, a study of the interaction between supervised and unsupervised learning has been carried out. We have studied and implemented models that apply clustering in multiple ways and in conjunction with classification to construct robust text classifiers. The extensive experimentation has shown the effectiveness of using clustering to boost text classification performance. Additionally, preliminary experiments on some of the most important applications of text classification such as Spam Mail Filtering, Spam Detection in Social Bookmarking Systems,and Sentence Boundary Disambiguation, have shown promising enhancements by exploiting the proposed models.

scholarly journals GCIceNet: a graph convolutional network for accurate classification of water phases

2020 ◽  
Vol 22 (45) ◽  
pp. 26340-26350
Author(s):  
QHwan Kim ◽  
Joon-Hyuk Ko ◽  
Sunghoon Kim ◽  
Wonho Jhe
Keyword(s):  
Unsupervised Learning ◽  
Order Parameters ◽  
Water Molecules ◽  
Convolutional Network ◽  
Convolutional Networks ◽  
Supervised And Unsupervised Learning

We develop GCIceNet, which automatically generates machine-based order parameters for classifying the phases of water molecules via supervised and unsupervised learning with graph convolutional networks.

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.

Classification of hyperspectral images based on multifractal features and support vector machine

2018 ◽  
Vol 62 (5) ◽  
pp. 558-562
Author(s):  
Uchaev D.V. ◽  
◽  
Uchaev Dm.V. ◽  
Malinnikov V.A. ◽  
◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Hyperspectral Images ◽  
Support Vector
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