Log in

Relevant bibliographies by topics / Data structure for quantum machine learning / Books

To see the other types of publications on this topic, follow the link: Data structure for quantum machine learning.

Books on the topic 'Data structure for quantum machine learning'

Author: Grafiati

Published: 5 June 2025

Last updated: 15 July 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 25 books for your research on the topic 'Data structure for quantum machine learning.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse books on a wide variety of disciplines and organise your bibliography correctly.

1

Michael, Affenzeller, ed. Genetic algorithms and genetic programming: Modern concepts and practical applications. Chapman & Hall/CRC, 2009.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

2

Wittek, Peter. Quantum Machine Learning: What Quantum Computing Means to Data Mining. Elsevier Science & Technology Books, 2016.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

3

Wittek, Peter. Quantum Machine Learning: What Quantum Computing Means to Data Mining. Elsevier Science & Technology Books, 2014.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

4

Quantum Machine Learning: What Quantum Computing Means to Data Mining. Elsevier Science & Technology Books, 2014.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

5

Rauf, Ijaz A. Physics of Data Science and Machine Learning. Taylor & Francis Group, 2021.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

6

Rauf, Ijaz A. Physics of Data Science and Machine Learning. Taylor & Francis Group, 2021.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

7

Rauf, Ijaz A. Physics of Data Science and Machine Learning. Taylor & Francis Group, 2021.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

8

Physics of Data Science and Machine Learning. CRC Press, 2021.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

9

Smith, Noah A. Linguistic Structure Prediction. Springer International Publishing AG, 2011.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

10

Linguistic structure prediction. Morgan & Claypool, 2011.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

11

Linguistic Structure Prediction. Springer Nature, 2011.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

12

Fiebrink, Rebecca A., and Baptiste Caramiaux. The Machine Learning Algorithm as Creative Musical Tool. Edited by Roger T. Dean and Alex McLean. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780190226992.013.23.

Full text

Abstract:

Machine learning is the capacity of a computational system to learn structure from data in order to make predictions on new data. This chapter draws on music, machine learning, and human-computer interaction to elucidate an understanding of machine learning algorithms as creative tools for music and the sonic arts. It motivates a new understanding of learning algorithms as human-computer interfaces: like other interfaces, learning algorithms can be characterized by the ways their affordances intersect with goals of human users. The chapter also argues that the nature of interaction between users and algorithms impacts the usability and usefulness of those algorithms in profound ways. This human-centred view of machine learning motivates a concluding discussion of what it means to employ machine learning as a creative tool.

APA, Harvard, Vancouver, ISO, and other styles

13

Nedjah, Nadia, Heitor Silverio Lopes, and Luiza De Macedo Mourelle. Evolutionary Multi-Objective System Design: Theory and Applications. Taylor & Francis Group, 2020.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

14

Nedjah, Nadia, Heitor Silverio Lopes, and Luiza De Macedo Mourelle. Evolutionary Multi-Objective System Design: Theory and Applications. Taylor & Francis Group, 2020.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

15

Nedjah, Nadia, Luiza de Macedo Mourelle, and Heitor Silvério Lopes. Evolutionary Multi-Objective System Design. Taylor & Francis Group, 2020.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

16

Evolutionary Multi-Objective System Design: Theory and Applications. Taylor & Francis Group, 2020.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

17

Sastry, Kumara, Martin Pelikan, and Erick Cantú-Paz. Scalable Optimization via Probabilistic Modeling: From Algorithms to Applications. Springer, 2010.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

18

Genetic Algorithms and Genetic Programming. CRC Press LLC, 2009.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

19

Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications (Numerical Insights). Chapman & Hall/CRC, 2008.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

20

Wagner, Stefan, Michael Affenzeller, Stephan Winkler, and Andreas Beham. Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications. Taylor & Francis Group, 2018.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

21

Wagner, Stefan, Michael Affenzeller, Stephan Winkler, and Andreas Beham. Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications. Taylor & Francis Group, 2009.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

22

Jockers, Matthew L. Theme. University of Illinois Press, 2017. http://dx.doi.org/10.5406/illinois/9780252037528.003.0008.

Full text

Abstract:

This chapter demonstrates how big data and computation can be used to identify and track recurrent themes as the products of external influence. It first considers the limitations of the Google Ngram Viewer as a tool for tracing thematic trends over time before turning to Douglas Biber's Corpus Linguistics: Investigating Language Structure and Use, a primer on various factors complicating word-focused text analysis and the subsequent conclusions one might draw regarding word meanings. It then discusses the results of the author's application of latent Dirichlet allocation (LDA) to a corpus of 3,346 nineteenth-century novels using the open-source MALLET (MAchine Learning for LanguagE Toolkit), a software package for topic modeling. It also explains the different types of analyses performed by the author, including text segmentation, word chunking, and author nationality, gender and time-themes relationship analyses. The thematic data from the LDA model reveal the degree to which author nationality, author gender, and date of publication could be predicted by the thematic signals expressed in the nineteenth-century novels corpus.

APA, Harvard, Vancouver, ISO, and other styles

23

Yang, Sijia, and Sandra González-Bailón. Semantic Networks and Applications in Public Opinion Research. Edited by Jennifer Nicoll Victor, Alexander H. Montgomery, and Mark Lubell. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780190228217.013.14.

Full text

Abstract:

Semantic networks represent and model messages and discourse as a relational structure, emphasizing patterns of interdependence among semantic units or actors-concepts. This chapter traces the epistemological roots of semantic networks, then illustrates with examples how this approach can contribute to the study of political rhetoric or opinions. It focuses on three levels of analysis: cognitive mapping at the individual level, discourse analysis at the interpersonal level, and framing and salience at the collective level. Drawing from the rich literature on natural language processing and machine learning, the chapter introduces readers to essential methodological considerations when extracting and building up semantic networks from textual data. It also offers a discussion on the relevance of semantic networks to analyzing public opinion, especially as it manifests in discursive and deliberative theories of democracy.

APA, Harvard, Vancouver, ISO, and other styles

24

Wikle, Christopher K. Spatial Statistics. Oxford University Press, 2018. http://dx.doi.org/10.1093/acrefore/9780190228620.013.710.

Full text

Abstract:

The climate system consists of interactions between physical, biological, chemical, and human processes across a wide range of spatial and temporal scales. Characterizing the behavior of components of this system is crucial for scientists and decision makers. There is substantial uncertainty associated with observations of this system as well as our understanding of various system components and their interaction. Thus, inference and prediction in climate science should accommodate uncertainty in order to facilitate the decision-making process. Statistical science is designed to provide the tools to perform inference and prediction in the presence of uncertainty. In particular, the field of spatial statistics considers inference and prediction for uncertain processes that exhibit dependence in space and/or time. Traditionally, this is done descriptively through the characterization of the first two moments of the process, one expressing the mean structure and one accounting for dependence through covariability.Historically, there are three primary areas of methodological development in spatial statistics: geostatistics, which considers processes that vary continuously over space; areal or lattice processes, which considers processes that are defined on a countable discrete domain (e.g., political units); and, spatial point patterns (or point processes), which consider the locations of events in space to be a random process. All of these methods have been used in the climate sciences, but the most prominent has been the geostatistical methodology. This methodology was simultaneously discovered in geology and in meteorology and provides a way to do optimal prediction (interpolation) in space and can facilitate parameter inference for spatial data. These methods rely strongly on Gaussian process theory, which is increasingly of interest in machine learning. These methods are common in the spatial statistics literature, but much development is still being done in the area to accommodate more complex processes and “big data” applications. Newer approaches are based on restricting models to neighbor-based representations or reformulating the random spatial process in terms of a basis expansion. There are many computational and flexibility advantages to these approaches, depending on the specific implementation. Complexity is also increasingly being accommodated through the use of the hierarchical modeling paradigm, which provides a probabilistically consistent way to decompose the data, process, and parameters corresponding to the spatial or spatio-temporal process.Perhaps the biggest challenge in modern applications of spatial and spatio-temporal statistics is to develop methods that are flexible yet can account for the complex dependencies between and across processes, account for uncertainty in all aspects of the problem, and still be computationally tractable. These are daunting challenges, yet it is a very active area of research, and new solutions are constantly being developed. New methods are also being rapidly developed in the machine learning community, and these methods are increasingly more applicable to dependent processes. The interaction and cross-fertilization between the machine learning and spatial statistics community is growing, which will likely lead to a new generation of spatial statistical methods that are applicable to climate science.

APA, Harvard, Vancouver, ISO, and other styles

25

Sangeetha, V., and S. Kevin Andrews. Introduction to Artificial Intelligence and Neural Networks. Magestic Technology Solutions (P) Ltd, Chennai, Tamil Nadu, India, 2023. http://dx.doi.org/10.47716/mts/978-93-92090-24-0.

Full text

Abstract:

Artificial Intelligence (AI) has emerged as a defining force in the current era, shaping the contours of technology and deeply permeating our everyday lives. From autonomous vehicles to predictive analytics and personalized recommendations, AI continues to revolutionize various facets of human existence, progressively becoming the invisible hand guiding our decisions. Simultaneously, its growing influence necessitates the need for a nuanced understanding of AI, thereby providing the impetus for this book, “Introduction to Artificial Intelligence and Neural Networks.” This book aims to equip its readers with a comprehensive understanding of AI and its subsets, machine learning and deep learning, with a particular emphasis on neural networks. It is designed for novices venturing into the field, as well as experienced learners who desire to solidify their knowledge base or delve deeper into advanced topics. In Chapter 1, we provide a thorough introduction to the world of AI, exploring its definition, historical trajectory, and categories. We delve into the applications of AI, and underscore the ethical implications associated with its proliferation. Chapter 2 introduces machine learning, elucidating its types and basic algorithms. We examine the practical applications of machine learning and delve into challenges such as overfitting, underfitting, and model validation. Deep learning and neural networks, an integral part of AI, form the crux of Chapter 3. We provide a lucid introduction to deep learning, describe the structure of neural networks, and explore forward and backward propagation. This chapter also delves into the specifics of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). In Chapter 4, we outline the steps to train neural networks, including data preprocessing, cost functions, gradient descent, and various optimizers. We also delve into regularization techniques and methods for evaluating a neural network model. Chapter 5 focuses on specialized topics in neural networks such as autoencoders, Generative Adversarial Networks (GANs), Long Short-Term Memory Networks (LSTMs), and Neural Architecture Search (NAS). In Chapter 6, we illustrate the practical applications of neural networks, examining their role in computer vision, natural language processing, predictive analytics, autonomous vehicles, and the healthcare industry. Chapter 7 gazes into the future of AI and neural networks. It discusses the current challenges in these fields, emerging trends, and future ethical considerations. It also examines the potential impacts of AI and neural networks on society. Finally, Chapter 8 concludes the book with a recap of key learnings, implications for readers, and resources for further study. This book aims not only to provide a robust theoretical foundation but also to kindle a sense of curiosity and excitement about the endless possibilities AI and neural networks offer. The journ

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!