Machine Learning, Optimization, and Data Science

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 LongestCommon 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 intensivecare 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.