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Dissertations / Theses on the topic 'Data structure for quantum machine learning'

Author: Grafiati

Published: 5 June 2025

Last updated: 15 July 2025

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1

ERBA, VITTORIO. "Aspects of data structure in machine learning." Doctoral thesis, Università degli Studi di Milano, 2021. http://hdl.handle.net/2434/873262.

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It is widely believed that understanding data structure is a crucial ingredient to push forward our comprehension on how (and why) modern machine learning works. Still, most of the theoretical results we have are obtained under very simplifying assumptions on the structure of the training data. In this Thesis, I review some novel results on the problem of characterizing the geometric structure of datasets and the consequences that this structure has on learning algorithms. I also provide pedagogical introductions to manifold learning, random geometric graphs theory and supervised binary c

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ERBA, VITTORIO. "ASPECTS OF DATA STRUCTURE IN MACHINE LEARNING." Doctoral thesis, Università degli Studi di Milano, 2021. http://hdl.handle.net/2434/873502.

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It is widely believed that understanding data structure is a crucial ingredient to push forward our comprehension on how (and why) modern machine learning works. Still, most of the theoretical results we have are obtained under very simplifying assumptions on the structure of the training data. In this Thesis, I review some novel results on the problem of characterizing the geometric structure of datasets and the consequences that this structure has on learning algorithms. I also provide pedagogical introductions to manifold learning, random geometric graphs theory and supervised binary c

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3

Lever, G. "Exploiting structure defined by data in machine learning : some new analyses." Thesis, University College London (University of London), 2011. http://discovery.ucl.ac.uk/1302070/.

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This thesis offers some new analyses and presents some new methods for learning in the context of exploiting structure defined by data – for example, when a data distribution has a submanifold support, exhibits cluster structure or exists as an object such as a graph. 1. We present a new PAC-Bayes analysis of learning in this context, which is sharp and in some ways presents a better solution than uniform convergence methods. The PAC-Bayes prior over a hypothesis class is defined in terms of the unknown true risk and smoothness of hypotheses w.r.t. the unknown data-generating distribution. The

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4

Hu, Hae-Jin. "Design of Comprehensible Learning Machine Systems for Protein Structure Prediction." Digital Archive @ GSU, 2007. http://digitalarchive.gsu.edu/cs_diss/22.

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With the efforts to understand the protein structure, many computational approaches have been made recently. Among them, the Support Vector Machine (SVM) methods have been recently applied and showed successful performance compared with other machine learning schemes. However, despite the high performance, the SVM approaches suffer from the problem of understandability since it is a black-box model; the predictions made by SVM cannot be interpreted as biologically meaningful way. To overcome this limitation, a new association rule based classifier PCPAR was devised based on the existing cla

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Straub, Kayla Marie. "Data Mining Academic Emails to Model Employee Behaviors and Analyze Organizational Structure." Thesis, Virginia Tech, 2016. http://hdl.handle.net/10919/71320.

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Email correspondence has become the predominant method of communication for businesses. If not for the inherent privacy concerns, this electronically searchable data could be used to better understand how employees interact. After the Enron dataset was made available, researchers were able to provide great insight into employee behaviors based on the available data despite the many challenges with that dataset. The work in this thesis demonstrates a suite of methods to an appropriately anonymized academic email dataset created from volunteers' email metadata. This new dataset, from an int

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Polianskii, Vladislav. "An Investigation of Neural Network Structure with Topological Data Analysis." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-238702.

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Artificial neural networks at the present time gain notable popularity and show astounding results in many machine learning tasks. This, however, also results in a drawback that the understanding of the processes happening inside of learning algorithms decreases. In many cases, the process of choosing a neural network architecture for a problem comes down to selection of network layers by intuition and to manual tuning of network parameters. Therefore, it is important to build a strong theoretical base in this area, both to try to reduce the amount of manual work in the future and to get a bet

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Abu-Hakmeh, Khaldoon Emad. "Assessing the use of voting methods to improve Bayesian network structure learning." Thesis, Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/45826.

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Structure inference in learning Bayesian networks remains an active interest in machine learning due to the breadth of its applications across numerous disciplines. As newer algorithms emerge to better handle the task of inferring network structures from observational data, network and experiment sizes heavily impact the performance of these algorithms. Specifically difficult is the task of accurately learning networks of large size under a limited number of observations, as often encountered in biological experiments. This study evaluates the performance of several leading structure learning

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Iqbal, Sumaiya. "Machine Learning based Protein Sequence to (un)Structure Mapping and Interaction Prediction." ScholarWorks@UNO, 2017. http://scholarworks.uno.edu/td/2379.

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Proteins are the fundamental macromolecules within a cell that carry out most of the biological functions. The computational study of protein structure and its functions, using machine learning and data analytics, is elemental in advancing the life-science research due to the fast-growing biological data and the extensive complexities involved in their analyses towards discovering meaningful insights. Mapping of protein’s primary sequence is not only limited to its structure, we extend that to its disordered component known as Intrinsically Disordered Proteins or Regions in proteins (IDPs/IDRs

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9

Chen, Jonathan Jun Feng. "Data Mining/Machine Learning Techniques for Drug Discovery: Computational and Experimental Pipeline Development." University of Akron / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=akron1524661027035591.

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Todorov, Helena. "Structure learning to unravel mechanisms of the immune system." Thesis, Lyon, 2020. http://www.theses.fr/2020LYSEN084.

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Les cellules de notre système immunitaire jouent un rôle essentiel en nous protégeant de pathogènes infectieux tels que les virus ou certaines bactéries. Lors d’une maladie, les différents types de cellules immunitaires jouent des rôles spécifiques et interagissent, générant ainsi une réponse immunitaire adéquate. Cependant, cette réponse immunitaire complexe peut parfois être perturbée. Par exemple, les cellules qui sont supposées combattre l’infection peuvent être rendues silencieuses. Ce phénomène est observé dans certaines tumeurs, dans lesquelles des cellules peuvent commencer à prolifére

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Kinalwa-Nalule, Myra. "Using machine learning to determine fold class and secondary structure content from Raman optical activity and Raman vibrational spectroscopy." Thesis, University of Manchester, 2012. https://www.research.manchester.ac.uk/portal/en/theses/using-machine-learning-to-determine-fold-class-and-secondary-structure-content-from-raman-optical-activity-and-raman-vibrational-spectroscopy(7382043d-748c-4d29-ba75-67fb35ccdb19).html.

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The objective of this project was to apply machine learning methods to determine protein secondary structure content and protein fold class from ROA and Raman vibrational spectral data. Raman and ROA are sensitive to biomolecular structure with the bands of each spectra corresponding to structural elements in proteins and when combined give a fingerprint of the protein. However, there are many bands of which little is known. There is a need, therefore, to find ways of extrapolating information from spectral bands and investigate which regions of the spectra contain the most useful structural i

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Li, Chuyuan. "Facing Data Scarcity in Dialogues for Discourse Structure Discovery and Prediction." Electronic Thesis or Diss., Université de Lorraine, 2023. http://www.theses.fr/2023LORR0107.

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Un document est plus qu'une combinaison aléatoire de phrases. Il s'agit plutôt d'une entité cohésive où les phrases interagissent les unes avec les autres pour créer une structure cohérente et transmettre des objectifs de communication spécifiques. Le domaine du discours examine l'organisation des phrases au sein d'un document, dans le but de révéler les informations structurelles sous-jacentes. L'analyse du discours joue un rôle crucial dans le Traitement Automatique des Langues (TAL) et a démontré son utilité dans diverses applications telles que le résumé et la question-réponse. Les efforts

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Cheng, Heng-Tze. "Learning and Recognizing The Hierarchical and Sequential Structure of Human Activities." Research Showcase @ CMU, 2013. http://repository.cmu.edu/dissertations/293.

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The mission of the research presented in this thesis is to give computers the power to sense and react to human activities. Without the ability to sense the surroundings and understand what humans are doing, computers will not be able to provide active, timely, appropriate, and considerate services to the humans. To accomplish this mission, the work stands on the shoulders of two giants: Machine learning and ubiquitous computing. Because of the ubiquity of sensor-enabled mobile and wearable devices, there has been an emerging opportunity to sense, learn, and infer human activities from the sen

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Linn, Hanna. "Detecting quantum speedup for random walks with artificial neural networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-289347.

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Random walks on graphs are an essential base for crucial algorithms for solving problems, like the boolean satisfiability problem. A speedup of random walks could improve these algorithms. The quantum version of the random walk, quantum walk, is faster than random walks in specific cases, e.g., on some linear graphs. An analysis of when the quantum walk is faster than the random walk can be accomplished analytically or by simulating both the walks on the graph. The problem arises when the graphs grow in size and connectivity. There are no known general rules for what an arbitrary graph not hav

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AMATO, Domenico. "A Tour of Learned Static Sorted Sets Dictionaries: From Specific to Generic with an Experimental Performance Analysis." Doctoral thesis, Università degli Studi di Palermo, 2022. http://hdl.handle.net/10447/554025.

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In recent years, in the era of Big Data, studying new methods to improve the performance of well-known procedures, such as searching in a Sorted Set, has become crucial in many fields. A new trend emerging in this scenario combines Machine Learning models with Data Structures, generating the so-called Learned Data Structures. In this thesis, we provide an in-depth experimental study of the use of these models, starting from some evidence known to experts in the field but not experimentally investigated concerning the use of very complex models such as Neural Networks. Then, we document a time/

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Chen, Xi. "Learning with Sparcity: Structures, Optimization and Applications." Research Showcase @ CMU, 2013. http://repository.cmu.edu/dissertations/228.

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The development of modern information technology has enabled collecting data of unprecedented size and complexity. Examples include web text data, microarray & proteomics, and data from scientific domains (e.g., meteorology). To learn from these high dimensional and complex data, traditional machine learning techniques often suffer from the curse of dimensionality and unaffordable computational cost. However, learning from large-scale high-dimensional data promises big payoffs in text mining, gene analysis, and numerous other consequential tasks. Recently developed sparse learning techniques p

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Louboutin, Corentin. "Modélisation multi-échelle et multi-dimensionnelle de la structure musicale par graphes polytopiques." Thesis, Rennes 1, 2019. http://www.theses.fr/2019REN1S012/document.

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Il est raisonnable de considérer qu'un auditeur ne perçoit pas la musique comme une simple séquence de sons, pas plus que le compositeur n'a conçu son morceau comme tel. La musique est en effet constituée de motifs dont l'organisation intrinsèque et les relations mutuelles participent à la structuration du propos musical, et ce à plusieurs échelles simultanément. Cependant, il est aujourd'hui encore très difficile de définir précisément le terme de concept musicale. L'un des principaux aspects de la musique est qu'elle est en grande partie constituée de redondances, sous forme de répétitions e

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Foulon, Lucas. "Détection d'anomalies dans les flux de données par structure d'indexation et approximation : Application à l'analyse en continu des flux de messages du système d'information de la SNCF." Thesis, Lyon, 2020. http://www.theses.fr/2020LYSEI082.

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Dans cette thèse, nous proposons des méthodes de calcul approchées d'un score d'anomalie, pouvant être mises en oeuvre sur des flux de données pour détecter des portions anormales. La difficulté du problème est de deux ordres. D'une part, la haute dimensionnalité des objets manipulés pour décrire les séries temporelles extraites d'un flux brut, et d'autre part la nécessité de limiter le coût de détection afin de pouvoir la réaliser en continu au fil du flux. Concernant le premier aspect du problème, notre étude bibliographique a permis de sélectionner un score de détection d'anomalies proposé

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Hecht, Robert. "Automatische Klassifizierung von Gebäudegrundrissen." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2014. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-151601.

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Für die Beantwortung verschiedener Fragestellungen im Siedlungsraum werden kleinräumige Informationen zur Siedlungsstruktur (funktional, morphologisch und sozio-ökonomisch) benötigt. Der Gebäudebestand spielt eine besondere Rolle, da dieser die physische Struktur prägt und sich durch dessen Nutzung Verteilungsmuster von Wohnungen, Arbeitsstätten und Infrastrukturen ergeben. In amtlichen Geodaten, Karten und Diensten des Liegenschaftskatasters und der Landesvermessung sind die Gebäude in ihrem Grundriss modelliert. Diese besitzen allerdings nur selten explizite semantische Informationen zum Geb

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20

Gabel, Sebastian. "One-to-One Marketing in Grocery Retailing." Doctoral thesis, Humboldt-Universität zu Berlin, 2019. http://dx.doi.org/10.18452/20084.

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In der akademischen Fachliteratur existieren kaum Forschungsergebnisse zu One-to-One-Marketing, die auf Anwendungen im Einzelhandel ausgerichtet sind. Zu den Hauptgründen zählen, dass Ansätze nicht auf die Größe typischer Einzelhandelsanwendungen skalieren und dass die Datenverfügbarkeit auf Händler und Marketing-Systemanbieter beschränkt ist. Die vorliegende Dissertation entwickelt neue deskriptive, prädiktive und präskriptive Modelle für automatisiertes Target Marketing, die auf Representation Learning und Deep Learning basieren, und untersucht deren Wirksamkeit in Praxisanwendungen. I

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Skyner, Rachael Elaine. "Hydrate crystal structures, radial distribution functions, and computing solubility." Thesis, University of St Andrews, 2017. http://hdl.handle.net/10023/11746.

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Solubility prediction usually refers to prediction of the intrinsic aqueous solubility, which is the concentration of an unionised molecule in a saturated aqueous solution at thermodynamic equilibrium at a given temperature. Solubility is determined by structural and energetic components emanating from solid-phase structure and packing interactions, solute–solvent interactions, and structural reorganisation in solution. An overview of the most commonly used methods for solubility prediction is given in Chapter 1. In this thesis, we investigate various approaches to solubility prediction and so

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Hecht, Robert. "Automatische Erkennung von Gebäudetypen auf Grundlage von Geobasisdaten." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2015. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-158993.

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Für die kleinräumige Modellierung und Analyse von Prozessen im Siedlungsraum spielen gebäudebasierte Informationen eine zentrale Rolle. In amtlichen Geodaten, Karten und Diensten des Liegenschaftskatasters und der Landesvermessung werden die Gebäude in ihrem Grundriss modelliert. Semantische Informationen zur Gebäudefunktion, der Wohnform oder dem Baualter sind in den Geobasisdaten nur selten gegeben. In diesem Beitrag wird eine Methode zur automatischen Klassifizierung von Gebäudegrundrissen vorgestellt mit dem Ziel, diese für die Ableitung kleinräumiger Informationen zur Siedlungsstruktur zu

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Hecht, Robert. "Automatische Klassifizierung von Gebäudegrundrissen: Ein Beitrag zur kleinräumigen Beschreibung der Siedlungsstruktur." Doctoral thesis, Leibniz-Institut für ökologische Raumentwicklung e. V. (IÖR), 2013. https://tud.qucosa.de/id/qucosa%3A28256.

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Für die Beantwortung verschiedener Fragestellungen im Siedlungsraum werden kleinräumige Informationen zur Siedlungsstruktur (funktional, morphologisch und sozio-ökonomisch) benötigt. Der Gebäudebestand spielt eine besondere Rolle, da dieser die physische Struktur prägt und sich durch dessen Nutzung Verteilungsmuster von Wohnungen, Arbeitsstätten und Infrastrukturen ergeben. In amtlichen Geodaten, Karten und Diensten des Liegenschaftskatasters und der Landesvermessung sind die Gebäude in ihrem Grundriss modelliert. Diese besitzen allerdings nur selten explizite semantische Informationen zum Geb

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Couetoux, Adrien. "Monte Carlo Tree Search pour les problèmes de décision séquentielle en milieu continus et stochastiques." Phd thesis, Université Paris Sud - Paris XI, 2013. http://tel.archives-ouvertes.fr/tel-00927252.

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Dans cette thèse, nous avons étudié les problèmes de décisions séquentielles, avec comme application la gestion de stocks d'énergie. Traditionnellement, ces problèmes sont résolus par programmation dynamique stochastique. Mais la grande dimension, et la non convexité du problème, amènent à faire des simplifications sur le modèle pour pouvoir faire fonctionner ces méthodes. Nous avons donc étudié une méthode alternative, qui ne requiert pas de simplifications du modèle: Monte Carlo Tree Search (MCTS). Nous avons commencé par étendre le MCTS classique (qui s'applique aux domaines finis et déterm

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Hecht, Robert. "Automatische Erkennung von Gebäudetypen auf Grundlage von Geobasisdaten." Rhombos-Verlag, 2013. https://slub.qucosa.de/id/qucosa%3A4903.

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Für die kleinräumige Modellierung und Analyse von Prozessen im Siedlungsraum spielen gebäudebasierte Informationen eine zentrale Rolle. In amtlichen Geodaten, Karten und Diensten des Liegenschaftskatasters und der Landesvermessung werden die Gebäude in ihrem Grundriss modelliert. Semantische Informationen zur Gebäudefunktion, der Wohnform oder dem Baualter sind in den Geobasisdaten nur selten gegeben. In diesem Beitrag wird eine Methode zur automatischen Klassifizierung von Gebäudegrundrissen vorgestellt mit dem Ziel, diese für die Ableitung kleinräumiger Informationen zur Siedlungsstruktur zu

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Gkirtzou, Aikaterini. "Sparsity regularization and graph-based representation in medical imaging." Phd thesis, Ecole Centrale Paris, 2013. http://tel.archives-ouvertes.fr/tel-00960163.

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Medical images have been used to depict the anatomy or function. Their high-dimensionality and their non-linearity nature makes their analysis a challenging problem. In this thesis, we address the medical image analysis from the viewpoint of statistical learning theory. First, we examine regularization methods for analyzing MRI data. In this direction, we introduce a novel regularization method, the k-support regularized Support Vector Machine. This algorithm extends the 1 regularized SVM to a mixed norm of both '1 and '2 norms. We evaluate our algorithm in a neuromuscular disease classificati

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Patrix, Jérémy. "Détection de comportements à travers des modèles multi-agents collaboratifs, appliquée à l'évaluation de la situation, notamment en environnement asymétrique avec des données imprécises et incertaines." Phd thesis, Université de Caen, 2013. http://tel.archives-ouvertes.fr/tel-00991091.

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Ce manuscrit de thèse présente une méthode innovante brevetée pour la détection de comportements collectifs. En utilisant des procédés de fusion sur les données issues d'un réseau multi-capteurs, les récents systèmes de surveillance obtiennent les séquences d'observations des personnes surveillées. Ce bas niveau d'évaluation de la situation a été mesuré insuffisant pour aider les forces de sécurité lors des événements de foule. Afin d'avoir une plus haute évaluation de la situation dans ces environnements asymétriques, nous proposons une approche multi-agents qui réduit la complexité du problè

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Laurence, Grégoire. "Normalisation et Apprentissage de Transductions d'Arbres en Mots." Phd thesis, Université des Sciences et Technologie de Lille - Lille I, 2014. http://tel.archives-ouvertes.fr/tel-01053084.

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Le stockage et la gestion de données sont des questions centrales en infor- matique. La structuration sous forme d'arbres est devenue la norme (XML, JSON). Pour en assurer la pérennité et l'échange efficace des données, il est nécessaire d'identifier de nouveaux mécanismes de transformations automati- sables. Nous nous concentrons sur l'étude de transformations d'arbres en mots représentées par des machines à états finies. Nous définissons les transducteurs séquentiels d'arbres en mots ne pouvant utiliser qu'une et unique fois chaque nœud de l'arbre d'entrée pour décider de la

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"Classical and quantum data sketching with applications in communication complexity and machine learning." 2014. http://repository.lib.cuhk.edu.hk/en/item/cuhk-1291567.

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Liu, Yang.<br>Thesis Ph.D. Chinese University of Hong Kong 2014.<br>Includes bibliographical references (leaves 163-188).<br>Abstracts also in Chinese.<br>Title from PDF title page (viewed on 25, October, 2016).

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Mengoni, Riccardo. "Quantum Approaches to Data Science and Data Analytics." Doctoral thesis, 2020. http://hdl.handle.net/11562/1018231.

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In this thesis are explored different research directions related to both the use of classical data analysis techniques for the study of quantum systems and the employment of quantum computing to speed up hard Machine Learning tasks

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Memisevic, Roland. "Non-linear Latent Factor Models for Revealing Structure in High-dimensional Data." Thesis, 2008. http://hdl.handle.net/1807/11118.

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Real world data is not random: The variability in the data-sets that arise in computer vision, signal processing and other areas is often highly constrained and governed by a number of degrees of freedom that is much smaller than the superficial dimensionality of the data. Unsupervised learning methods can be used to automatically discover the “true”, underlying structure in such data-sets and are therefore a central component in many systems that deal with high-dimensional data. In this thesis we develop several new approaches to modeling the low-dimensional structure in data. We introduce a

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"Exploring Latent Structure in Data: Algorithms and Implementations." Doctoral diss., 2014. http://hdl.handle.net/2286/R.I.27464.

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abstract: Feature representations for raw data is one of the most important component in a machine learning system. Traditionally, features are \textit{hand crafted} by domain experts which can often be a time consuming process. Furthermore, they do not generalize well to unseen data and novel tasks. Recently, there have been many efforts to generate data-driven representations using clustering and sparse models. This dissertation focuses on building data-driven unsupervised models for analyzing raw data and developing efficient feature representations. Simultaneous segmentation and feature e

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Yadav, BKV. "Mapping species composition and structure in wet eucalypt forest using multi-source remote sensing data." Thesis, 2019. https://eprints.utas.edu.au/34651/1/Yadav_whole_thesis.pdf.

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Tasmanian wet eucalypt forests are internationally important for wood and paper production, carbon storage and biodiversity conservation. These forests contain tall eucalypts over dense understories of rainforest and wet sclerophyll species. This research was motivated by a need for tools to replace costly aerial photo interpretation (PI-type) mapping for describing forest species composition and stand structure. Overall, I aimed to develop approaches for assessing and mapping tree species distribution and forest structure of wet eucalypt forest in a 5 km by 5 km area of the Warra Supersite, T

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Mihalkova, Lilyana Simeonova. "Learning with Markov logic networks : transfer learning, structure learning, and an application to Web query disambiguation." 2009. http://hdl.handle.net/2152/10574.

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Traditionally, machine learning algorithms assume that training data is provided as a set of independent instances, each of which can be described as a feature vector. In contrast, many domains of interest are inherently multi-relational, consisting of entities connected by a rich set of relations. For example, the participants in a social network are linked by friendships, collaborations, and shared interests. Likewise, the users of a search engine are related by searches for similar items and clicks to shared sites. The ability to model and reason about such relations is essential not only b

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(5930474), Rongrong Zhang. "Chromosome 3D Structure Modeling and New Approaches For General Statistical Inference." Thesis, 2019.

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<div>This thesis consists of two separate topics, which include the use of piecewise helical models for the inference of 3D spatial organizations of chromosomes and new approaches for general statistical inference. The recently developed Hi-C technology enables a genome-wide view of chromosome</div><div>spatial organizations, and has shed deep insights into genome structure and genome function. However, multiple sources of uncertainties make downstream data analysis and interpretation challenging. Specically, statistical models for inferring three-dimensional (3D) chromosomal structure from Hi

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Bhattacharya, Sourangshu. "Computational Protein Structure Analysis : Kernel And Spectral Methods." Thesis, 2008. https://etd.iisc.ac.in/handle/2005/831.

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The focus of this thesis is to develop computational techniques for analysis of protein structures. We model protein structures as points in 3-dimensional space which in turn are modeled as weighted graphs. The problem of protein structure comparison is posed as a weighted graph matching problem and an algorithm motivated from the spectral graph matching techniques is developed. The thesis also proposes novel similarity measures by deriving kernel functions. These kernel functions allow the data to be mapped to a suitably defined Reproducing kernel Hilbert Space(RKHS), paving the way for effic

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Bhattacharya, Sourangshu. "Computational Protein Structure Analysis : Kernel And Spectral Methods." Thesis, 2008. http://hdl.handle.net/2005/831.

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The focus of this thesis is to develop computational techniques for analysis of protein structures. We model protein structures as points in 3-dimensional space which in turn are modeled as weighted graphs. The problem of protein structure comparison is posed as a weighted graph matching problem and an algorithm motivated from the spectral graph matching techniques is developed. The thesis also proposes novel similarity measures by deriving kernel functions. These kernel functions allow the data to be mapped to a suitably defined Reproducing kernel Hilbert Space(RKHS), paving the way for effic

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