Sketchbook Francisco Herrera T01

Herrera, Francisco

TRINQUETTE PUBL

Francisco's last volume in the series. Includes robotics, Star Wars, Spider-Man and other Marvel characters, women of all shapes and sizes, animals, even two Gruau homage pieces! About half the book is pencil drawings and the other half evenly divided between black &amp; white and color finished pieces.

Sketchbook Francisco Herrera T04

Comics & Graphic Novels

Manga

Sketchbook Francisco Herrera T02

Sketchbook Francisco Herrera T03

This&nbsp; book provides a general and comprehensible&nbsp;overview of&nbsp;&nbsp; imbalanced learning.&nbsp; It contains a formal description of a problem, and focuses on its main features, and the most relevant proposed solutions. Additionally, it considers&nbsp;the different scenarios in Data Science for which the imbalanced classification can&nbsp;create a real challenge.&nbsp; This book stresses the gap with standard classification tasks by reviewing the case&nbsp;studies and ad-hoc performance metrics that are applied in this area. It also covers the&nbsp;different approaches that have been traditionally applied to address the binary&nbsp;skewed class distribution. Specifically, it reviews cost-sensitive learning, data-level&nbsp;preprocessing methods and algorithm-level solutions, taking also into account those&nbsp;ensemble-learning solutions that embed any of the former alternatives. Furthermore, it&nbsp;focuses on the extension of the problem for multi-class problems, where the former&nbsp;classical methods are no longer to be applied in a straightforward way. This book also focuses on the data intrinsic characteristics that are the main causes&nbsp;which, added to the uneven class distribution, truly hinders the performance of&nbsp;classification algorithms in this scenario. Then, some notes on data reduction are&nbsp;provided in order to understand the advantages related to the use of this type of approaches. Finally this book introduces some novel areas of study that are gathering a deeper attention&nbsp;on the imbalanced data issue. Specifically, it considers the classification of data streams,&nbsp;non-classical classification problems, and the scalability related to Big Data. Examples&nbsp;of software libraries and modules to address imbalanced classification are provided. This book is highly suitable for technical professionals, senior&nbsp;undergraduate and graduate&nbsp;students in the areas of data science,&nbsp;computer science and engineering.&nbsp;&nbsp;It will also be useful for scientists and researchers to gain insight on the current&nbsp;developments in this area of study, as well as future research directions.&nbsp;

Learning from Imbalanced Data Sets

Computers & Technology

García, Salvador

Fernández, Alberto

Galar, Mikel

Prati, Ronaldo C.

Krawczyk, Bartosz

Computer Science

Springer

This book examines one of the more common and wide-spread methodologies to deal with uncertainty in real-world decision making problems, the computing with words paradigm, and the fuzzy linguistic approach. The 2-tuple linguistic model is the most popular methodology for computing with words (CWW), because it improves the accuracy of the linguistic computations and keeps the interpretability of the results.   The authors provide a thorough review of the specialized literature in CWW and highlight the rapid growth and applicability of the 2-tuple linguistic model. They explore the foundations and methodologies for CWW in complex frameworks and extensions. The book introduces the software FLINTSTONES that provides tools for solving linguistic decision problems based on the 2-tuple linguistic model.  Professionals and researchers working in the field of classification or fuzzy sets and systems will find The 2-tuple Linguistic Model: Computing with Words in Decision Making a valuable resource. Undergraduate and postdoctoral students studying computer science and statistics will also find this book a useful study guide.

The 2-tuple Linguistic Model: Computing with Words in Decision Making

Martínez, Luis

Rodriguez, Rosa M.

Data Preprocessing for Data Mining addresses one of the most important issues within the well-known Knowledge Discovery from Data process. Data directly taken from the source will likely have inconsistencies, errors or most importantly, it is not ready to be considered for a data mining process. Furthermore, the increasing amount of data in recent science, industry and business applications, calls to the requirement of more complex tools to analyze it. Thanks to data preprocessing, it is possible to convert the impossible into possible, adapting the data to fulfill the input demands of each data mining algorithm. Data preprocessing includes the data reduction techniques, which aim at reducing the complexity of the data, detecting or removing irrelevant and noisy elements from the data.
This book is intended to review the tasks that fill the gap between the data acquisition from the source and the data mining process. A comprehensive look from a practical point of view, including basic concepts and surveying the techniques proposed in the specialized literature, is given.Each chapter is a stand-alone guide to a particular data preprocessing topic, from basic concepts and detailed descriptions of classical algorithms, to an incursion of an exhaustive catalog of recent developments. The in-depth technical descriptions make this book suitable for technical professionals, researchers, senior undergraduate and graduate students in data science, computer science and engineering.

Data Preprocessing in Data Mining (Intelligent Systems Reference Library, 72)

Luengo, Julián

This book offers a comprehensive review of multilabel techniques widely used to classify and label texts, pictures, videos and music in the Internet. A deep review of the specialized literature on the field includes the available software needed to work with this kind of data. It provides the user with the software tools needed to deal with multilabel data, as well as step by step instruction on how to use them. The main topics covered are: • The special characteristics of multi-labeled data and the metrics available to measure them.• The importance of taking advantage of label correlations to improve the results.• The different approaches followed to face multi-label classification.• The preprocessing techniques applicable to multi-label datasets.• The available software tools to work with multi-label data. This book is beneficial for professionals and researchers in a variety of fields because of the wide range of potential applications for multilabel classification. Besides its multiple applications to classify different types of online information, it is also useful in many other areas, such as genomics and biology. No previous knowledge about the subject is required. The book introduces all the needed concepts to understand multilabel data characterization, treatment and evaluation.

Multilabel Classification: Problem Analysis, Metrics and Techniques

Charte, Francisco

Rivera, Antonio J.

del Jesus, María J.

This book provides a general overview of multiple instance learning (MIL), defining the framework and covering the central paradigms. The authors discuss the most important algorithms for MIL such as classification, regression and clustering. With a focus on classification, a taxonomy is set and the most relevant proposals are specified. Efficient algorithms are developed to discover relevant information when working with uncertainty. Key representative applications are included. This book carries out a study of the key related fields of distance metrics and alternative hypothesis. Chapters examine new and developing aspects of MIL such as data reduction for multi-instance problems and imbalanced MIL data. Class imbalance for multi-instance problems is defined at the bag level, a type of representation that utilizes ambiguity due to the fact that bag labels are available, but the labels of the individual instances are not defined. Additionally, multiple instance multiple label learning is explored. This learning framework introduces flexibility and ambiguity in the object representation providing a natural formulation for representing complicated objects. Thus, an object is represented by a bag of instances and is allowed to have associated multiple class labels simultaneously. This book is suitable for developers and engineers working to apply MIL techniques to solve a variety of real-world problems. It is also useful for researchers or students seeking a thorough overview of MIL literature, methods, and tools.

Multiple Instance Learning: Foundations and Algorithms

Ventura, Sebastián

Bello, Rafael

Cornelis, Chris

Zafra, Amelia

Sánchez-Tarragó, Dánel

Vluymans, Sarah

This book offers a comprehensive review of multilabel techniques widely used to classify and label texts, pictures, videos and music in the Internet. A deep review of the specialized literature on the field includes the available software needed to work with this kind of data. It provides the user with the software tools needed to deal with multilabel data, as well as step by step instruction on how to use them. The main topics covered are: -The special characteristics of multi-labeled data and the metrics available to measure them.-The importance of taking advantage of label correlations to improve the results.-The different approaches followed to face multi-label classification.-The preprocessing techniques applicable to multi-label datasets.-The available software tools to work with multi-label data.This book is beneficial for professionals and researchers in a variety of fields because of the wide range of potential applications for multilabel classification. Besides its multiple applications to classify different types of online information, it is also useful in many other areas, such as genomics and biology. No previous knowledge about the subject is required. The book introduces all the needed concepts to understand multilabel data characterization, treatment and evaluation.

See the best price to sell, buy, or rent books by Herrera, Francisco. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

Books by Herrera, Francisco