Multilabel Classification With Label Structures
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Author |
: Wei Bi |
Publisher |
: |
Total Pages |
: 130 |
Release |
: 2015 |
ISBN-10 |
: OCLC:945483096 |
ISBN-13 |
: |
Rating |
: 4/5 (96 Downloads) |
Synopsis Multilabel Classification with Label Structures by : Wei Bi
Author |
: Jinseok Nam |
Publisher |
: |
Total Pages |
: |
Release |
: 2019 |
ISBN-10 |
: OCLC:1137035410 |
ISBN-13 |
: |
Rating |
: 4/5 (10 Downloads) |
Synopsis Learning Label Structures with Neural Networks for Multi-label Classification by : Jinseok Nam
Author |
: Francisco Herrera |
Publisher |
: Springer |
Total Pages |
: 200 |
Release |
: 2016-08-09 |
ISBN-10 |
: 9783319411118 |
ISBN-13 |
: 331941111X |
Rating |
: 4/5 (18 Downloads) |
Synopsis Multilabel Classification by : Francisco Herrera
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.
Author |
: Liang Sun |
Publisher |
: CRC Press |
Total Pages |
: 206 |
Release |
: 2016-04-19 |
ISBN-10 |
: 9781439806166 |
ISBN-13 |
: 1439806160 |
Rating |
: 4/5 (66 Downloads) |
Synopsis Multi-Label Dimensionality Reduction by : Liang Sun
Similar to other data mining and machine learning tasks, multi-label learning suffers from dimensionality. An effective way to mitigate this problem is through dimensionality reduction, which extracts a small number of features by removing irrelevant, redundant, and noisy information. The data mining and machine learning literature currently lacks
Author |
: Hendrik Blockeel |
Publisher |
: Springer |
Total Pages |
: 240 |
Release |
: 2012-05-31 |
ISBN-10 |
: 1461411467 |
ISBN-13 |
: 9781461411468 |
Rating |
: 4/5 (67 Downloads) |
Synopsis Predictive Clustering by : Hendrik Blockeel
This book introduces a novel paradigm for machine learning and data mining called predictive clustering, which covers a broad variety of learning tasks and offers a fresh perspective on existing techniques. The book presents an informal introduction to predictive clustering, describing learning tasks and settings, and then continues with a formal description of the paradigm, explaining algorithms for learning predictive clustering trees and predictive clustering rules, as well as presenting the applicability of these learning techniques to a broad range of tasks. Variants of decision tree learning algorithms are also introduced. Finally, the book offers several significant applications in ecology and bio-informatics. The book is written in a straightforward and easy-to-understand manner, aimed at varied readership, ranging from researchers with an interest in machine learning techniques to practitioners of data mining technology in the areas of ecology and bioinformatics.
Author |
: Tidake Santosh Vaishali |
Publisher |
: |
Total Pages |
: 0 |
Release |
: 2023-10-16 |
ISBN-10 |
: 9798889954798 |
ISBN-13 |
: |
Rating |
: 4/5 (98 Downloads) |
Synopsis Progressive Multi-Label Classification Algorithm by : Tidake Santosh Vaishali
Progressive Multi-Label Classification (PMLC) is a machine learning technique designed to address complex classification problems where each instance can belong to multiple categories simultaneously. Unlike traditional multi-label classification, PMLC takes into account the hierarchical nature of labels and the order in which labels are predicted, allowing for a more efficient and accurate classification process. In PMLC, labels are organized in a hierarchy or a taxonomy, reflecting the relationships between them. This hierarchy is often represented as a directed acyclic graph (DAG), where parent labels represent broader categories, and child labels represent more specific subcategories. The key idea behind PMLC is to make the classification process progressive, meaning that labels are predicted in a structured order, starting from the most general and moving towards the most specific labels. This approach is advantageous because it reduces the label space's dimensionality and makes predictions more interpretable. The PMLC process typically involves two main stages: training and prediction. During the training stage, a model is trained using the hierarchical label structure. The model learns to predict labels in a progressive manner by starting with the root of the hierarchy and moving down towards the leaf nodes. This hierarchical training process is often done using a top-down or bottom-up approach, where either the most general or the most specific labels are predicted first. The choice of approach depends on the problem and the structure of the label hierarchy. One common algorithm used in PMLC is the hierarchical classifier chain (HCC). In HCC, each label is associated with a separate binary classifier. Labels are ordered based on the hierarchical structure, and each classifier is trained to predict its corresponding label, taking into account the predictions of its ancestor labels. This way, the classifiers use the information from higher-level labels to assist in predicting lower-level labels. This progressive prediction mechanism aligns with the hierarchical structure and ensures that the predictions respect the relationships between labels.
Author |
: Andrea Torsello |
Publisher |
: Springer Nature |
Total Pages |
: 384 |
Release |
: 2021-04-09 |
ISBN-10 |
: 9783030739737 |
ISBN-13 |
: 3030739732 |
Rating |
: 4/5 (37 Downloads) |
Synopsis Structural, Syntactic, and Statistical Pattern Recognition by : Andrea Torsello
This book constitutes the proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition, S+SSPR 2020, held in Padua, Italy, in January 2021. The 35 papers presented in this volume were carefully reviewed and selected from 81 submissions. The accepted papers cover the major topics of current interest in pattern recognition, including classification and clustering, deep learning, structural matching and graph-theoretic methods, and multimedia analysis and understanding.
Author |
: Adam Krzyzak |
Publisher |
: Springer Nature |
Total Pages |
: 336 |
Release |
: 2023-01-01 |
ISBN-10 |
: 9783031230288 |
ISBN-13 |
: 3031230280 |
Rating |
: 4/5 (88 Downloads) |
Synopsis Structural, Syntactic, and Statistical Pattern Recognition by : Adam Krzyzak
This book constitutes the proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition, S+SSPR 2022, held in Montreal, QC, Canada, in August 2022. The 30 papers together with 2 invited talks presented in this volume were carefully reviewed and selected from 50 submissions. The workshops presents papers on topics such as deep learning, processing, computer vision, machine learning and pattern recognition and much more.
Author |
: Jason Brownlee |
Publisher |
: Machine Learning Mastery |
Total Pages |
: 266 |
Release |
: 2016-05-13 |
ISBN-10 |
: |
ISBN-13 |
: |
Rating |
: 4/5 ( Downloads) |
Synopsis Deep Learning With Python by : Jason Brownlee
Deep learning is the most interesting and powerful machine learning technique right now. Top deep learning libraries are available on the Python ecosystem like Theano and TensorFlow. Tap into their power in a few lines of code using Keras, the best-of-breed applied deep learning library. In this Ebook, learn exactly how to get started and apply deep learning to your own machine learning projects.
Author |
: IEEE Staff |
Publisher |
: |
Total Pages |
: |
Release |
: 2019-11-04 |
ISBN-10 |
: 1728137993 |
ISBN-13 |
: 9781728137995 |
Rating |
: 4/5 (93 Downloads) |
Synopsis 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI) by : IEEE Staff
ICTAI 2019 The IEEE International Conference on Tools with Artificial Intelligence (ICTAI) is a leading Conference of AI in the Computer Society providing a major international forum where the creation and exchange of ideas related to artificial intelligence are fostered among academia, industry, and government agencies The conference facilitates the cross fertilization of AI ideas and promotes their transfer into practical tools, for developing intelligent systems and pursuing artificial intelligence applications The ICTAI encompasses all technical aspects of specifying, developing and evaluating the theoretical underpinnings and applied mechanisms of the AI based components of computer tools (i e algorithms, architectures or languages)