2,035 results for machine learning
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Machine learning and hyper spectral imaging: multi spectral endoscopy in the gastro intestinal tract towards hyper spectral endoscopy
Worldwide, carcinomas are the second leading cause of death after cardiovascular diseases. Of these, more than 25 % are caused by cancer in the gastrointestinal tract. However, screening still misses about 20 % of carcinomas. An optimal solution would be a red flag technology to locate the…
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Inteligencia Artificial Aplicada con técnicas de Procesamiento de Lenguaje Natural y Machine Learning en el campo de la salud.
La Inteligencia Artificial (IA), el Procesamiento del Lenguaje Natural (NLP) y el Aprendizaje Automático (ML) han jugado un papel crucial en la lucha contra la pandemia de Covid-19, proporcionando herramientas tecnológicas valiosas para el diagnóstico, seguimiento y control de la enfermedad…
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Hyperparameter Tuning for Machine and Deep Learning with R
…in practice and gives deep insights into the working mechanisms of machine learning (ML) and deep learning (DL) methods. The aim of the book is to equip readers with the ability to achieve better results with significantly less time, costs, effort and resources using the methods described here. The…
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Chapter Mapping Yucca gloriosa in coastal dunes: evaluating the cost and time efficiency of photointerpretation, machine learning and field detection approaches
…methods for detecting and mapping alien plants: photointerpretation, machine learning classification, and field monitoring. Yucca gloriosa L., an invasive species in Regional Park of Migliarino-San Rossore-Massaciuccoli (Tuscany, Italy), served as the target species. Using RGB DJI Phantom 4 Pro v…
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Chapter Vision Goes Symbolic Without Loss of Information Within the Preattentive Vision Phase: The Need to Shift the Learning Paradigm from Machine-Learning (from Examples) to Machine-Teaching (by Rules) at the First Stage of a Two-Stage Hybrid Remote... Part II
Optical physics
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Book Chapter Full text
Chapter Vision Goes Symbolic Without Loss of Information Within the Preattentive Vision Phase: The Need to Shift the Learning Paradigm from Machine-Learning (from Examples) to Machine-Teaching (by Rules) at the First Stage of a Two-Stage Hybrid Remote... Part I
Life sciences: general issues
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Human Machine Interaction
In this book, the reader will find a set of papers divided into two sections. The first section presents different proposals focused on the human-machine interaction development process. The second section is devoted to different aspects of interaction, with a special emphasis on the physical…
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Deep Learning
Deep learning has revolutionized numerous fields due to its ability to process vast amounts of data and learn complex patterns. Its importance lies in its capacity to tackle tasks that were difficult or impossible for traditional algorithms. In health care, for example, it aids in diagnosing…
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Deep Learning and Reinforcement Learning
Deep learning and reinforcement learning are some of the most important and exciting research fields today. With the emergence of new network structures and algorithms such as convolutional neural networks, recurrent neural networks, and self-attention models, these technologies have gained…
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Human-Centric Machine Vision
…real-world environments. These achievements yield to the development of Machine Vision systems that overcome the typical industrial applications, where the environments are controlled and the tasks are very specific, towards the use of innovative solutions to face with everyday needs of people. The…
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Robot Learning
Robot Learning is intended for one term advanced Machine Learning courses taken by students from different computer science research disciplines. This text has all the features of a renowned best selling text. It gives a focused introduction to the primary themes in a Robot learning course and…
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Active Learning
Active learning is now a form of learning that accompanies the knowledge evolution that challenges the learner to promote it, but also encourages him to investigate and become emotionally involved in the task. The great key to obtaining this behavior successfully depends, therefore, on the…
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Deep Learning Applications
Deep learning is a branch of machine learning similar to artificial intelligence. The applications of deep learning vary from medical imaging to industrial quality checking, sports, and precision agriculture. This book is divided into two sections. The first section covers deep learning…
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Distributional Reinforcement Learning
The first comprehensive guide to distributional reinforcement learning, providing a new mathematical formalism for thinking about decisions from a probabilistic perspective.Distributional reinforcement learning is a new mathematical formalism for thinking about decisions. Going beyond the common…
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Learning to Quantify
This open access book provides an introduction and an overview of learning to quantify (a.k.a. “quantification”), i.e. the task of training estimators of class proportions in unlabeled data by means of supervised learning. In data science, learning to quantify is a task of its own related to…
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The Perception Machine
A provocative investigation of the future of photography and human perception in the age of AI.We are constantly photographing and being photographed while feeding machine learning databases with our data, which in turn is used to generate new images. Analyzing the transformation of photography by…
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Digital Interaction and Machine Intelligence
This book is open access, which means that you have free and unlimited access. This book presents the Proceedings of the 9th Machine Intelligence and Digital Interaction Conference. Significant progress in the development of artificial intelligence (AI) and its wider use in many interactive…
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Machine translation for everyone
Language learning and translation have always been complementary pillars of multilingualism in the European Union. Both have been affected by the increasing availability of machine translation (MT): language learners now make use of free online MT to help them both understand and produce texts in a…
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Manifold Learning
…synthetic data forecast by these models, experimental data and deep learning algorithms. The book involves a survey of key methods of model order reduction applied to model-based engineering and digital twining, by learning linear or nonlinear latent spaces. Projection-based reduced order models…
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Advances in Reinforcement Learning
Reinforcement Learning (RL) is a very dynamic area in terms of theory and application. This book brings together many different aspects of the current research on several fields associated to RL which has been growing rapidly, producing a wide variety of learning algorithms for different…
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