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

The smallest extraction problem

2021 ◽  
Vol 14 (11) ◽  
pp. 2445-2458
Author(s):  
Valerio Cetorelli ◽  
Paolo Atzeni ◽  
Valter Crescenzi ◽  
Franco Milicchio
Keyword(s):  
Unsupervised Learning ◽  
Optimization Problem ◽  
Learning Algorithm ◽  
State Of The Art ◽  
Data Extraction ◽  
Source Code ◽  
Web Data ◽  
Web Data Extraction ◽  
New Family ◽  
Context Free

We introduce landmark grammars , a new family of context-free grammars aimed at describing the HTML source code of pages published by large and templated websites and therefore at effectively tackling Web data extraction problems. Indeed, they address the inherent ambiguity of HTML, one of the main challenges of Web data extraction, which, despite over twenty years of research, has been largely neglected by the approaches presented in literature. We then formalize the Smallest Extraction Problem (SEP), an optimization problem for finding the grammar of a family that best describes a set of pages and contextually extract their data. Finally, we present an unsupervised learning algorithm to induce a landmark grammar from a set of pages sharing a common HTML template, and we present an automatic Web data extraction system. The experiments on consolidated benchmarks show that the approach can substantially contribute to improve the state-of-the-art.

scholarly journals A Statistical Model for Word Discovery in Transcribed Speech

2001 ◽  
Vol 27 (3) ◽  
pp. 351-372 ◽  
Author(s):  
Anand Venkataraman
Keyword(s):  
Statistical Model ◽  
Unsupervised Learning ◽  
Learning Algorithm ◽  
Continuous Speech ◽  
Word Boundaries ◽  
Empirical Tests

A statistical model for segmentation and word discovery in continuous speech is presented. An incremental unsupervised learning algorithm to infer word boundaries based on this model is described. Results are also presented of empirical tests showing that the algorithm is competitive with other models that have been used for similar tasks.

scholarly journals Clustering fMRI data with a robust unsupervised learning algorithm for neuroscience data mining

2018 ◽  
Vol 299 ◽  
pp. 45-54 ◽  
Author(s):  
Hadeel K. Aljobouri ◽  
Hussain A. Jaber ◽  
Orhan M. Koçak ◽  
Oktay Algin ◽  
Ilyas Çankaya
Keyword(s):  
Data Mining ◽  
Unsupervised Learning ◽  
Learning Algorithm ◽  
Fmri Data

LAMDA-HAD, an Extension to the LAMDA Classifier in the Context of Supervised Learning

2020 ◽  
Vol 19 (01) ◽  
pp. 283-316 ◽  
Author(s):  
Luis Morales ◽  
José Aguilar ◽  
Danilo Chávez ◽  
Claudia Isaza
Keyword(s):  
Data Analysis ◽  
Statistical Analysis ◽  
Unsupervised Learning ◽  
Supervised Learning ◽  
Learning Algorithm ◽  
New Approach ◽  
Supervised And Unsupervised Learning ◽  
Training Stage

This paper proposes a new approach to improve the performance of Learning Algorithm for Multivariable Data Analysis (LAMDA). This algorithm can be used for supervised and unsupervised learning, based on the calculation of the Global Adequacy Degree (GAD) of one individual to a class, through the contributions of all its descriptors. LAMDA has the capability of creating new classes after the training stage. If an individual does not have enough similarity to the preexisting classes, it is evaluated with respect to a threshold called the Non-Informative Class (NIC), this being the novelty of the algorithm. However, LAMDA has problems making good classifications, either because the NIC is constant for all classes, or because the GAD calculation is unreliable. In this work, its efficiency is improved by two strategies, the first one, by the calculation of adaptable NICs for each class, which prevents that correctly classified individuals create new classes; and the second one, by computing the Higher Adequacy Degree (HAD), which grants more robustness to the algorithm. LAMDA-HAD is validated by applying it in different benchmarks and comparing it with LAMDA and other classifiers, through a statistical analysis to determinate the cases in which our algorithm presents a better performance.

scholarly journals Approach for 3D Cultural Relic Classification Based on a Low-Dimensional Descriptor and Unsupervised Learning

Entropy ◽  
2020 ◽  
Vol 22 (11) ◽  
pp. 1290
Author(s):  
Hongjuan Gao ◽  
Guohua Geng ◽  
Sheng Zeng
Keyword(s):  
Unsupervised Learning ◽  
Heat Kernel ◽  
Learning Algorithm ◽  
Heat Diffusion ◽  
Uneven Distribution ◽  
Actual Case ◽  
Category Labels ◽  
Cultural Relic ◽  
Heat Kernel Signature ◽  
Low Dimensional

Computer-aided classification serves as the basis of virtual cultural relic management and display. The majority of the existing cultural relic classification methods require labelling of the samples of the dataset; however, in practical applications, there is often a lack of category labels of samples or an uneven distribution of samples of different categories. To solve this problem, we propose a 3D cultural relic classification method based on a low dimensional descriptor and unsupervised learning. First, the scale-invariant heat kernel signature (Si-HKS) was computed. The heat kernel signature denotes the heat flow of any two vertices across a 3D shape and the heat diffusion propagation is governed by the heat equation. Secondly, the Bag-of-Words (BoW) mechanism was utilized to transform the Si-HKS descriptor into a low-dimensional feature tensor, named a SiHKS-BoW descriptor that is related to entropy. Finally, we applied an unsupervised learning algorithm, called MKDSIF-FCM, to conduct the classification task. A dataset consisting of 3D models from 41 Tang tri-color Hu terracotta Eures was utilized to validate the effectiveness of the proposed method. A series of experiments demonstrated that the SiHKS-BoW descriptor along with the MKDSIF-FCM algorithm showed the best classification accuracy, up to 99.41%, which is a solution for an actual case with the absence of category labels and an uneven distribution of different categories of data. The present work promotes the application of virtual reality in digital projects and enriches the content of digital archaeology.

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