Machine Learning, Optimization, and Data Science : 5th International Conference, LOD 2019, Siena, Italy, September 10-13, 2019, Proceedings / edited by Giuseppe Nicosia, Panos Pardalos, Renato Umeton, Giovanni Giuffrida, Vincenzo Sciacca.

Format:

Book

Contributor:

Nicosia, Giuseppe, Editor.

Pardalos, Panos., Editor.

Umeton, Renato, Editor.

Giuffrida, Giovanni, Editor.

Sciacca, Vincenzo, Editor.

SpringerLink (Online service)

Series:

Computer Science (SpringerNature-11645)

LNCS sublibrary. Information systems and applications, incl. Internet/Web, and HCI ; SL 3, 11943

Information Systems and Applications, incl. Internet/Web, and HCI ; 11943

Language:

English

Subjects (All):

Application software.

Artificial intelligence.

Data mining.

Computer and Information Systems Applications.

Artificial Intelligence.

Data Mining and Knowledge Discovery.

Local Subjects:

Computer and Information Systems Applications.

Artificial Intelligence.

Data Mining and Knowledge Discovery.

Physical Description:

1 online resource (XXVI, 772 pages) : 225 illustrations, 160 illustrations in color.

Edition:

1st ed. 2019.

Contained In:

Springer Nature eBook

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2019.

System Details:

text file PDF

Summary:

This book constitutes the post-conference proceedings of the 5th International Conference on Machine Learning, Optimization, and Data Science, LOD 2019, held in Siena, Italy, in September 2019. The 54 full papers presented were carefully reviewed and selected from 158 submissions. The papers cover topics in the field of machine learning, artificial intelligence, reinforcement learning, computational optimization and data science presenting a substantial array of ideas, technologies, algorithms, methods and applications.

Contents:

Deep Neural Network Ensembles

Driver Distraction Detection Using Deep Neural Network

Deep Learning Algorithms for Complex Pattern Recognition in Ultrasonic Sensors Arrays

An Information Analysis Approach into Feature Understanding of Convolutional Deep Neural Networks

Stochastic Weight Matrix-based Regularization Methods for Deep Neural Networks

Quantitative and Ontology-Based Comparison of Explanations for Image Classification

About generative aspects of Variational Autoencoders

Adapted Random Survival Forest for Histograms to Analyze NOx Sensor Failure in Heavy Trucks

Incoherent submatrix selection via approximate independence sets in scalar product graphs

LIA: A Label-Independent Algorithm for Feature Selection for Supervised Learning

Relationship Estimation Metrics for Binary SoC Data

Network Alignment using Graphlet Signature and High Order Proximity

Effect of Market Spread over Reinforcement Learning based Market Maker

A Beam Search for the Longest Common Subsequence Problem Guided by a Novel Approximate Expected Length Calculation

An Adaptive Parameter Free Particle Swarm Optimization Algorithm for the Permutation Flowshop Scheduling Problem

The measure of regular relations recognition applied to the supervised classification task

Simple and Accurate classifi cation method based on Class Association Rules performs well on well-known datasets

Analyses of Multi-collection Corpora via Compound Topic Modeling

Text mining with constrained tensor decomposition

The induction problem: a machine learning vindication argument

Geospatial Dimension in Association Rule Mining: The Case Study of the Amazon Charcoal Tree

On Probabilistic k-Richness of the k-Means Algorithms

Using clustering for supervised feature selection to detect relevant features

A Structural Theorem for Center-Based Clustering in High-Dimensional Euclidean Space

Modification of the k-MXT Algorithm and Its Application to the Geotagged Data Clustering

CoPASample: A Heuristics based Covariance Preserving Data Augmentation

Active Matrix Completion for Algorithm Selection

A Framework for Multi- delity Modeling in Global Optimization Approaches

Performance Evaluation of Local Surrogate Models in Bilevel Optimization

BowTie - a deep learning feedforward neural network for sentiment analysis

To What Extent Can Text Classifiation Help with Making Inferences About Students' Understanding

Combinatorial Learning in Traffic Management

Cartesian Genetic Programming with Guided and Single Active Mutations for Designing Combinational Logic Circuits

Designing an Optimal and Resilient iBGP Overlay with extended ORRTD

GRASP Heuristics for the Stochastic Weighted Graph Fragmentation Problem

Uniformly Most-Reliable Graphs and Antiholes

Merging Quality Estimation for Binary Decision Diagrams with Binary Classfi ers

Directed Acyclic Graph Reconstruction Leveraging Prior Partial Ordering Information

Learning Scale and Shift-Invariant Dictionary for Sparse Representation

Robust kernelized Bayesian matrix factorization for video background/foreground separation

Parameter Optimization of Polynomial Kernel SVM from miniCV

Analysing the Over t of the auto-sklearn Automated Machine Learning Tool

A New Baseline for Automated Hyper-Parameter Optimization

Optimal trade-o between sample size and precision of supervision for the xed effects panel data model

Restaurant Health Inspections and Crime Statistics Predict the Real Estate Market in New York City

Load Forecasting in District Heating Networks: Model Comparison on a Real-World Case Study

A Chained Neural Network Model for Photovoltaic Power Forecast

Trading-o Data Fit and Complexity in Training Gaussian Processes with Multiple Kernels

Designing Combinational Circuits Using a Multi-objective Cartesian Genetic Programming with Adaptive Population Size

Multi-Task Learning by Pareto Optimality Nicosia

Vital prognosis of patients in intensive care units using an Ensemble of Bayesian Classifiers

On the role of hub and orphan genes in the diagnosis of breast invasive carcinoma

Approximating Probabilistic Constraints for Surgery Scheduling using Neural Networks

Determining Principal Component Cardinality through the Principle of Minimum Description Length

Modelling chaotic time series using recursive deep self-organising neural networks

On Tree-based Methods for Similarity Learning

Active Learning Approach for Safe Process Parameter Tuning

Federated Learning of Deep Neural Decision Forests

Data Anonymization for Privacy aware Machine Learning

Exploiting Similar Behavior of Users in a Cooperative Optimization Approach for Distributing Service Points in Mobility Applications

Long Short-Term Memory Networks for Earthquake Detection in Venezuelan Regions

Zero-Shot Fashion Products Clustering on Social Image Streams

Treating Arti cial Neural Net Training as a Nonsmooth Global Optimization Problem.

Other Format:

Printed edition:

ISBN:

978-3-030-37599-7

9783030375997

Access Restriction:

Restricted for use by site license.