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974 tulosta hakusanalla "Vector"

Learning with Fractional Orthogonal Kernel Classifiers in Support Vector Machines

Learning with Fractional Orthogonal Kernel Classifiers in Support Vector Machines

SPRINGER VERLAG, SINGAPORE
2023
sidottu
This book contains select chapters on support vector algorithms from different perspectives, including mathematical background, properties of various kernel functions, and several applications. The main focus of this book is on orthogonal kernel functions, and the properties of the classical kernel functions—Chebyshev, Legendre, Gegenbauer, and Jacobi—are reviewed in some chapters. Moreover, the fractional form of these kernel functions is introduced in the same chapters, and for ease of use for these kernel functions, a tutorial on a Python package named ORSVM is presented. The book also exhibits a variety of applications for support vector algorithms, and in addition to the classification, these algorithms along with the introduced kernel functions are utilized for solving ordinary, partial, integro, and fractional differential equations. On the other hand, nowadays, the real-time and big data applications of support vector algorithms are growing. Consequently, the Compute Unified Device Architecture (CUDA) parallelizing the procedure of support vector algorithms based on orthogonal kernel functions is presented. The book sheds light on how to use support vector algorithms based on orthogonal kernel functions in different situations and gives a significant perspective to all machine learning and scientific machine learning researchers all around the world to utilize fractional orthogonal kernel functions in their pattern recognition or scientific computing problems.
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Learning with Fractional Orthogonal Kernel Classifiers in Support Vector Machines

Learning with Fractional Orthogonal Kernel Classifiers in Support Vector Machines

SPRINGER VERLAG, SINGAPORE
2024
nidottu
This book contains select chapters on support vector algorithms from different perspectives, including mathematical background, properties of various kernel functions, and several applications. The main focus of this book is on orthogonal kernel functions, and the properties of the classical kernel functions—Chebyshev, Legendre, Gegenbauer, and Jacobi—are reviewed in some chapters. Moreover, the fractional form of these kernel functions is introduced in the same chapters, and for ease of use for these kernel functions, a tutorial on a Python package named ORSVM is presented. The book also exhibits a variety of applications for support vector algorithms, and in addition to the classification, these algorithms along with the introduced kernel functions are utilized for solving ordinary, partial, integro, and fractional differential equations. On the other hand, nowadays, the real-time and big data applications of support vector algorithms are growing. Consequently, the Compute Unified Device Architecture (CUDA) parallelizing the procedure of support vector algorithms based on orthogonal kernel functions is presented. The book sheds light on how to use support vector algorithms based on orthogonal kernel functions in different situations and gives a significant perspective to all machine learning and scientific machine learning researchers all around the world to utilize fractional orthogonal kernel functions in their pattern recognition or scientific computing problems.
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Anti-Cancer Drug Resistance Causal Modeling from Lentiviral-Vector Integration Site Studies

Anti-Cancer Drug Resistance Causal Modeling from Lentiviral-Vector Integration Site Studies

Giulio Spinozzi

Eliva Press
2025
pokkari
Evolution plays a key role in cancer as the result of the accumulation of genetic alterations, which provide selective advantages to a tumor cell, allowing resistance to anti-cancer drugs. Unfortunately, however, the identification of the driver mutations and thus the mechanisms underlying anti-cancer drug resistance (ACDR) still remains a challenge. We previously demonstrated that lentiviral vectors (LVs), when properly modified, might integrate near specific genes, alter their expression and induce cancer or ACDR in vivo and in vitro. The analysis of vector-cellular genomic junctions in tumor or ACDR cells allowed identifying causative genes of HER2+ breast cancer cell line using a statistical approach defined Common Insertion Sites (CISs) that highlight genomic regions targeted at significantly higher frequency than expected by a random distribution. The reconstruction of cumulative cancer progression from CIS genes has not been yet addressed and may produce causative gene networks. The aim of this project is studying anti-cancer drug resistance from exclusive and co-occurring genes using cumulative cancer progression from cell line CIS genes and investigating the relation between them.
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Banach-hilbert Spaces, Vector Measures And Group Representations

Banach-hilbert Spaces, Vector Measures And Group Representations

Tsoy-wo Ma

World Scientific Publishing Co Pte Ltd
2002
sidottu
This book provides an elementary introduction to classical analysis on normed spaces, with special attention paid to fixed points, calculus, and ordinary differential equations. It contains a full treatment of vector measures on delta rings without assuming any scalar measure theory and hence should fit well into existing courses. The relation between group representations and almost periodic functions is presented. The mean values offer an infinitedimensional analogue of measure theory on finitedimensional Euclidean spaces. This book is ideal for beginners who want to get through the basic material as soon as possible and then do their own research immediately.
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Foremost Section Study on Brain Images Classifier using Kernel Base Support Vector Machine

Foremost Section Study on Brain Images Classifier using Kernel Base Support Vector Machine

Yashika Mathur

Eliva Press
2025
pokkari
Automated and accurate classification of MR brain images is extremely important for medical analysis and interpretation. Over the last decade numerous methods have already been proposed. In this thesis presented a novel method to classify a given MRI brain image as normal or abnormal. The proposed method first employed wavelet transform to extract features from images, followed by applying principle component analysis (PCA) to reduce the dimensions of features.The reduced features were submitted to a kernel support vector machine (KSVM). The strategy of K-fold stratified cross validation was used to enhance generalization of KSVM. Chose seven common brain diseases (glioma, meningioma, Alzheimer's disease, Alzheimer's disease plus visual agnosia, Pick'sdisease, sarcoma, and Huntington's disease) as abnormal brains, and collected 160 MR brain images (20 normal and 140 abnormal) from Signal and Image Processing Institute Electrical Engineering and Biomedical Engineering University of Southern California and Gokuldash Hospital Laborites Indore India. We performed our proposed methods with four different kernels, and found that the GRB kernel achieves the highest classification accuracy as 99.38%.We also compared our method to those from literatures in the last decade, and the results showed our DWT+PCA+KSVM with GRB kernels till achieved the best accurate classification results. The averaged processing time for a 256 256 size image on a laptop of I3 HP with 3 GHz processor and 4 GB RAM is 0.0448 s.It could be applied to the field of MR brain image classification and can assist the doctors to diagnose where a patient is normal or abnormal to certain degrees.
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Algebra Through Practice: Volume 2, Matrices and Vector Spaces

Algebra Through Practice: Volume 2, Matrices and Vector Spaces

Cambridge University Press
1984
pokkari
Problem solving is an art that is central to understanding and ability in mathematics. With this series of books the authors have provided a selection of problems with complete solutions and test papers designed to be used with or instead of standard textbooks on algebra. For the convenience of the reader, a key explaining how the present books may be used in conjunction with some of the major textbooks is included. Each book of problems is divided into chapters that begin with some notes on notation and prerequisites. The majority of the material is aimed at the student of average ability but there are some more challenging problems. By working through the books, the student will gain a deeper understanding of the fundamental concepts involved, and practice in the formulation, and so solution, of other algebraic problems. Later books in the series cover material at a more advanced level than the earlier titles, although each is, within its own limits, self-contained.
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Winter School on Mirror Symmetry, Vector Bundels and Lagrangian Submanifolds

Winter School on Mirror Symmetry, Vector Bundels and Lagrangian Submanifolds

Winter School on Mirror Symmetry (1999 Harvard University); Shing-Tung (EDT) Yau; Cumrun (EDT) Vafa

Amer Mathematical Society
2002
pokkari
The collection of articles in this volume are based on lectures presented during the Winter School on Mirror Symmetry held at Harvard University. There are many new directions suggested by mirror symmetry which could potentially have very rich connections in physics and mathematics. This book brings together the latest research in a major area of mathematical physics, including the recent progress in mirror manifolds and Lagrangian submanifolds. In particular, several articles describing homological approach and related topics are included. Other AMS titles edited by S.-T Yau published in the AMS/IP Studies in Advanced Mathematics series include, Mirror Symmetry III, Volume 10, Mirror symmetry II, Volume 1, and Mirror Symmetry I, Volume 9.
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The Sardinian Project: An Experiment in the Eradication of an Indigenous Malarious Vector

The Sardinian Project: An Experiment in the Eradication of an Indigenous Malarious Vector

John Alexander Logan; Dean Rusk; Marston Bates

Literary Licensing, LLC
2012
sidottu
The Sardinian Project: An Experiment In The Eradication Of An Indigenous Malarious Vector is a book written by John Alexander Logan. It is a detailed account of an experiment conducted in Sardinia in the early 1950s, which aimed to eradicate an indigenous mosquito species that was responsible for the transmission of malaria. The book describes the background of the project, the methods used, and the results obtained. It also discusses the impact of the project on the local population and the environment. The author provides a comprehensive analysis of the project, including its successes and failures, and draws lessons for future efforts to control malaria and other vector-borne diseases. The book is written in a clear and accessible style, making it suitable for both experts and non-experts in the field of public health. It is a valuable resource for anyone interested in the history of malaria control and the challenges of designing and implementing effective vector control programs.Additional Contributors Include Thomas H. G. Aitken, Guido U. Casini, Frederick W. Knipe, John Maier, Athol J. Patterson And Harold Trapido. The American Journal Of Hygiene Monographic Series, No. 20.This scarce antiquarian book is a facsimile reprint of the old original and may contain some imperfections such as library marks and notations. Because we believe this work is culturally important, we have made it available as part of our commitment for protecting, preserving, and promoting the world's literature in affordable, high quality, modern editions, that are true to their original work.
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Who Need A SUPER HERO, When You Are Vector Illustrator: 6X9 Career Pride 120 pages Writing Notebooks

Who Need A SUPER HERO, When You Are Vector Illustrator: 6X9 Career Pride 120 pages Writing Notebooks

Emma Loren

Independently Published
2019
nidottu
Super Hero Theme Motivational Quotes for Men & women, Perfect Notebook for people who Love their job. Write all your Daily weekly monthly yearly short and long term Goals, Activities and Schedule in this Notebook Journal.120 pages of 6x9 Journal is the perfect size and easy to handle. You can Gift this Career Journal to Your Friends Family or Colleagues.
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