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Learning Representation for Multi-View Data Analysis

, unbalanced and large-scale multi-view learning. Learning Representation for Multi-View Data Analysis covers a wide range of applications in the;

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Tensor Computation for Data Analysis

Tensor is a natural representation for multi-dimensional data, and tensor computation can avoid possible multi-linear data structure loss;

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Robust Representation for Data Analytics

unsupervised learning, supervised learning, semi-supervised learning, multi-view learning, transfer learning, and deep learning. Robust Representations;

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Behavior Analysis with Machine Learning Using R

ensemble learning methods and use Multi-View Stacking to fuse signals from heterogeneous data sources. Use unsupervised learning;

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Uncertainty Modelling and Quality Control for Spatial Data

of photogrammetric and LiDAR datasets based on a vector approach An analysis on the uncertainty of multi-scale representation for street-block;

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Uncertainty Modelling and Quality Control for Spatial Data

of photogrammetric and LiDAR datasets based on a vector approach An analysis on the uncertainty of multi-scale representation for street-block;

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Multi-disciplinary Trends in Artificial Intelligence

from 82 submissions. They are organized in the following topical sections: knowledge representation and reasoning; data mining and machine;

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Low Rank and Sparse Modeling for Visual Analysis

This book provides a view of low-rank and sparse computing, especially approximation, recovery, representation, scaling, coding, embedding;

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Foundations of Intelligent Systems

, recommender systems; machine learning; knowledge representation, semantic web; emotion recognition, music information retrieval; network analysis;

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Inductive Logic Programming

logic, ILP provides an excellent means for multi-relational learning and data mining, and more generally for learning from structured data.;

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Inductive Logic Programming

provides an excellent means for multi-relational learning and data mining, and more generally for learning from structured data.;

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Graph Representation Learning

this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings;

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Advances in Knowledge Discovery and Data Mining

, unstructured and semi-structured data; sentiment analysis; statistical/graphical model; multi-source/distributed/parallel/cloud computing.;

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Advances in Knowledge Discovery and Data Mining

, unstructured and semi-structured data; sentiment analysis; statistical/graphical model; multi-source/distributed/parallel/cloud computing.;

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New Approaches for Multidimensional Signal Processing

-view visualization, forensic analysis systems for MD images and many more.;

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Representation Learning for Natural Language Processing

This open access book provides an overview of the recent advances in representation learning theory, algorithms and applications for;

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Representation Learning for Natural Language Processing

This open access book provides an overview of the recent advances in representation learning theory, algorithms and applications for;

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Sparse representation of High Dimensional Data for Classification

In this book you will find the use of sparse Principal Component Analysis (PCA) for representing high dimensional data for classification;

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Algorithmic Foundation of Multi-Scale Spatial Representation

mathematical groundwork for multi-scale representations of spatial data.;

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Hyperspectral Image Analysis: Advances in Machine Learning and Signal Processing

analysis tasks, with a focus on emerging trends in machine learning and image processing/understanding. It presents advances in deep learning;

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Trends in Deep Learning Methodologies

predictions about future events. The major advantage of deep learning is to process big data analytics for better analysis and self-adaptive;

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Dynamic Fuzzy Machine Learning

Machine learning is widely used for data analysis. Dynamic fuzzy data are one of the most difficult types of data to analyse in the field;

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Hyperspectral Image Analysis

analysis tasks, with a focus on emerging trends in machine learning and image processing/understanding. It presents advances in deep learning;

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Foundations of Data Science

algorithms for machine learning, algorithms and analysis for clustering, probabilistic models for large networks, representation learning including;

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Discovery Science

knowledge, coming from machine learning, data mining, intelligent data analysis, big data analysis as well as their application in various;

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Knowledge Discovery in Multiple Databases

framework of local pattern analysis. The book is intended for researchers and students in data mining, dis tributed data analysis, machine learning;

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Machine Learning in Medical Imaging

, multi-view learning, manifold learning, and reinforcement learning, with their applications to medical image analysis, computer-aided detection;

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