Neural Network Enhanced Visualization Of High Dimensional Data
DOWNLOAD
Download Neural Network Enhanced Visualization Of High Dimensional Data PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Neural Network Enhanced Visualization Of High Dimensional Data book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page
Neural Network Enhanced Visualization Of High Dimensional Data
DOWNLOAD
Author : Urska Cvek
language : en
Publisher:
Release Date : 2010
Neural Network Enhanced Visualization Of High Dimensional Data written by Urska Cvek and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with categories.
We provide a description of an approach for intelligent spatial placement of highdimensional records, based on a modified Kohonen SOM algorithm. SOM-augmented visualizations provide for increased visual scalability and offer higher intrinsic dimension than the classic visualizations. The resulting mapping efficiently alleviates crowding and occlusion, and emphasizes the relationships among the neighbouring multi-dimensional records. Utilizing these techniques, the user can efficiently approach perceptual ambiguities associated with occlusion and gain insight into multi-dimensional data sets using an informative visualization, instead of a series of linked visualizations. These algorithms were tested on high-dimensional biomedical data sets and have provided for meaningful associations in an interactive environment.
Neural Network Machine Learning And Dimension Reduction For Data Visualization
DOWNLOAD
Author : National Aeronautics and Space Adm Nasa
language : en
Publisher: Independently Published
Release Date : 2019-01-13
Neural Network Machine Learning And Dimension Reduction For Data Visualization written by National Aeronautics and Space Adm Nasa and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-01-13 with Science categories.
Neural network machine learning in computer science is a continuously developing field of study. Although neural network models have been developed which can accurately predict a numeric value or nominal classification, a general purpose method for constructing neural network architecture has yet to be developed. Computer scientists are often forced to rely on a trial-and-error process of developing and improving accurate neural network models. In many cases, models are constructed from a large number of input parameters. Understanding which input parameters have the greatest impact on the prediction of the model is often difficult to surmise, especially when the number of input variables is very high. This challenge is often labeled the "curse of dimensionality" in scientific fields. However, techniques exist for reducing the dimensionality of problems to just two dimensions. Once a problem's dimensions have been mapped to two dimensions, it can be easily plotted and understood by humans. The ability to visualize a multi-dimensional dataset can provide a means of identifying which input variables have the highest effect on determining a nominal or numeric output. Identifying these variables can provide a better means of training neural network models; models can be more easily and quickly trained using only input variables which appear to affect the outcome variable. The purpose of this project is to explore varying means of training neural networks and to utilize dimensional reduction for visualizing and understanding complex datasets. Liles, Charles A. Langley Research Center NASA/TM-2014-218181, L-20389, NF1676L-18414
Diffusion Based Approaches To Visualization And Exploration Of High Dimensional Data
DOWNLOAD
Author : Scott Anthony Gigante
language : en
Publisher:
Release Date : 2021
Diffusion Based Approaches To Visualization And Exploration Of High Dimensional Data written by Scott Anthony Gigante and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with Bioinformatics categories.
In recent years, modern technologies have enabled the collection of exponentially larger quantities of data in the biomedical domain and elsewhere. In particular, the advent of single-cell genomics has allowed for the collection of datasets containing hundreds of thousands of cells measured in tens of thousands of dimensions. This rapid expansion of common datasets beyond the possibility of manual annotation brings forth the need for large-scale exploratory data analysis. In this thesis, we will explore the problem of dimensionality reduction for visualization of high-dimensional datasets. Visualization of high-dimensional data is an essential task in exploratory data analysis, as the low-dimensional visualization of the data is used to understand, interrogate and present the results of many other analyses applied to the data. However, the repertoire of existing algorithms used for this task suffer from various algorithmic flaws leading to sub-optimal visualizations, including the trade-off between representing both local and global structure; the inherent sacrifices that must be made to reduce a dataset of intrinsic dimension greater than three to a form which can be interpreted by the human eye; and the computational complexity of the computations as the datasets increase in scale. Here, we use the framework provided by diffusion maps to present a new dimensionality reduction algorithm called PHATE, which seeks to address all three of these issues. In order to make the PHATE algorithm scalable, we present an approximation of the diffusion map through discrete partitions of the data called Compression-based Fast Diffusion Maps. Further, we use the insights gained from visualizing single-cell genomics data to present a manifold alignment algorithm called Harmonic Alignment, which allows for the correction of systemic differences between experiments, or the fusion of datasets collected from the same biological system using different assays. And finally, we present an extension of PHATE to longitudinal data, and demonstrate its utility for the purpose of machine learning interpretability by visualizing the hidden units of a neural network in training. While many open problems remain, the presentation of the methods herein chart a path towards a more systematic understanding of how we visualize high-dimensional data for exploratory data analysis.
Fusion Methods For Unsupervised Learning Ensembles
DOWNLOAD
Author : Bruno Baruque
language : en
Publisher: Springer Science & Business Media
Release Date : 2010-11-23
Fusion Methods For Unsupervised Learning Ensembles written by Bruno Baruque and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-11-23 with Computers categories.
The application of a “committee of experts” or ensemble learning to artificial neural networks that apply unsupervised learning techniques is widely considered to enhance the effectiveness of such networks greatly. This book examines the potential of the ensemble meta-algorithm by describing and testing a technique based on the combination of ensembles and statistical PCA that is able to determine the presence of outliers in high-dimensional data sets and to minimize outlier effects in the final results. Its central contribution concerns an algorithm for the ensemble fusion of topology-preserving maps, referred to as Weighted Voting Superposition (WeVoS), which has been devised to improve data exploration by 2-D visualization over multi-dimensional data sets. This generic algorithm is applied in combination with several other models taken from the family of topology preserving maps, such as the SOM, ViSOM, SIM and Max-SIM. A range of quality measures for topology preserving maps that are proposed in the literature are used to validate and compare WeVoS with other algorithms. The experimental results demonstrate that, in the majority of cases, the WeVoS algorithm outperforms earlier map-fusion methods and the simpler versions of the algorithm with which it is compared. All the algorithms are tested in different artificial data sets and in several of the most common machine-learning data sets in order to corroborate their theoretical properties. Moreover, a real-life case-study taken from the food industry demonstrates the practical benefits of their application to more complex problems.
Advances In Natural Computation
DOWNLOAD
Author : Ke Chen
language : en
Publisher: Springer Science & Business Media
Release Date : 2005-08-17
Advances In Natural Computation written by Ke Chen and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-08-17 with Computers categories.
Annotation The three volume set LNCS 3610, LNCS 3611, and LNCS 3612 constitutes the refereed proceedings of the First International Conference on Natural Computation, ICNC 2005, held in Changsha, China, in August 2005 as a joint event in federation with the Second International Conference on Fuzzy Systems and Knowledge Discovery FSKD 2005 (LNAI volumes 3613 and 3614). The program committee selected 313 carefully revised full papers and 189 short papers for presentation in three volumes from 1887 submissions. The first volume includes all the contributions related to learning algorithms and architectures in neural networks, neurodynamics, statistical neural network models and support vector machines, and other topics in neural network models; cognitive science, neuroscience informatics, bioinformatics, and bio-medical engineering, and neural network applications as communications and computer networks, expert system and informatics, and financial engineering. The second volume concentrates on neural network applications such as pattern recognition and diagnostics, robotics and intelligent control, signal processing and multi-media, and other neural network applications; evolutionary learning, artificial immune systems, evolutionary theory, membrane, molecular, DNA computing, and ant colony systems. The third volume deals with evolutionary methodology, quantum computing, swarm intelligence and intelligent agents; natural computation applications as bioinformatics and bio-medical engineering, robotics and intelligent control, and other applications of natural computation; hardware implementations of natural computation, and fuzzy neural systems as well as soft computing.
Computational Intelligence And Healthcare Informatics
DOWNLOAD
Author : Om Prakash Jena
language : en
Publisher: John Wiley & Sons
Release Date : 2021-10-19
Computational Intelligence And Healthcare Informatics written by Om Prakash Jena and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-10-19 with Computers categories.
COMPUTATIONAL INTELLIGENCE and HEALTHCARE INFORMATICS The book provides the state-of-the-art innovation, research, design, and implements methodological and algorithmic solutions to data processing problems, designing and analysing evolving trends in health informatics, intelligent disease prediction, and computer-aided diagnosis. Computational intelligence (CI) refers to the ability of computers to accomplish tasks that are normally completed by intelligent beings such as humans and animals. With the rapid advance of technology, artificial intelligence (AI) techniques are being effectively used in the fields of health to improve the efficiency of treatments, avoid the risk of false diagnoses, make therapeutic decisions, and predict the outcome in many clinical scenarios. Modern health treatments are faced with the challenge of acquiring, analyzing and applying the large amount of knowledge necessary to solve complex problems. Computational intelligence in healthcare mainly uses computer techniques to perform clinical diagnoses and suggest treatments. In the present scenario of computing, CI tools present adaptive mechanisms that permit the understanding of data in difficult and changing environments. The desired results of CI technologies profit medical fields by assembling patients with the same types of diseases or fitness problems so that healthcare facilities can provide effectual treatments. This book starts with the fundamentals of computer intelligence and the techniques and procedures associated with it. Contained in this book are state-of-the-art methods of computational intelligence and other allied techniques used in the healthcare system, as well as advances in different CI methods that will confront the problem of effective data analysis and storage faced by healthcare institutions. The objective of this book is to provide researchers with a platform encompassing state-of-the-art innovations; research and design; implementation of methodological and algorithmic solutions to data processing problems; and the design and analysis of evolving trends in health informatics, intelligent disease prediction and computer-aided diagnosis. Audience The book is of interest to artificial intelligence and biomedical scientists, researchers, engineers and students in various settings such as pharmaceutical & biotechnology companies, virtual assistants developing companies, medical imaging & diagnostics centers, wearable device designers, healthcare assistance robot manufacturers, precision medicine testers, hospital management, and researchers working in healthcare system.
Integrating Artificial Intelligence And Visualization For Visual Knowledge Discovery
DOWNLOAD
Author : Boris Kovalerchuk
language : en
Publisher: Springer Nature
Release Date : 2022-06-04
Integrating Artificial Intelligence And Visualization For Visual Knowledge Discovery written by Boris Kovalerchuk and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-04 with Technology & Engineering categories.
This book is devoted to the emerging field of integrated visual knowledge discovery that combines advances in artificial intelligence/machine learning and visualization/visual analytic. A long-standing challenge of artificial intelligence (AI) and machine learning (ML) is explaining models to humans, especially for live-critical applications like health care. A model explanation is fundamentally human activity, not only an algorithmic one. As current deep learning studies demonstrate, it makes the paradigm based on the visual methods critically important to address this challenge. In general, visual approaches are critical for discovering explainable high-dimensional patterns in all types in high-dimensional data offering "n-D glasses," where preserving high-dimensional data properties and relations in visualizations is a major challenge. The current progress opens a fantastic opportunity in this domain. This book is a collection of 25 extended works of over 70 scholars presented at AI and visual analytics related symposia at the recent International Information Visualization Conferences with the goal of moving this integration to the next level. The sections of this book cover integrated systems, supervised learning, unsupervised learning, optimization, and evaluation of visualizations. The intended audience for this collection includes those developing and using emerging AI/machine learning and visualization methods. Scientists, practitioners, and students can find multiple examples of the current integration of AI/machine learning and visualization for visual knowledge discovery. The book provides a vision of future directions in this domain. New researchers will find here an inspiration to join the profession and to be involved for further development. Instructors in AI/ML and visualization classes can use it as a supplementary source in their undergraduate and graduate classes.
Computational Intelligence For Missing Data Imputation Estimation And Management Knowledge Optimization Techniques
DOWNLOAD
Author : Marwala, Tshilidzi
language : en
Publisher: IGI Global
Release Date : 2009-04-30
Computational Intelligence For Missing Data Imputation Estimation And Management Knowledge Optimization Techniques written by Marwala, Tshilidzi and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-04-30 with Computers categories.
"This book is for those who use data analysis to build decision support systems, particularly engineers, scientists and statisticians"--Provided by publisher.
Enhancing Computer Security With Smart Technology
DOWNLOAD
Author : V. Rao Vemuri
language : en
Publisher: CRC Press
Release Date : 2005-11-21
Enhancing Computer Security With Smart Technology written by V. Rao Vemuri and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-11-21 with Computers categories.
Divided into two major parts, Enhancing Computer Security with Smart Technology introduces the problems of computer security to researchers with a machine learning background, then introduces machine learning concepts to computer security professionals. Realizing the massive scope of these subjects, the author concentrates on problems relat
Visualization And Data Analysis
DOWNLOAD
Author :
language : en
Publisher:
Release Date : 2006
Visualization And Data Analysis written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006 with Computer graphics categories.