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Improved Classification Rates for Localized Algorithms under Margin Conditions
Support vector machines (SVMs) are one of the most successful algorithms on small and medium-sized data sets, but on large-scale data sets their...
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Robust Feedback Set Stabilization of Logic Networks with State-Dependent Uncertain Switching and Control Constraints
This study investigates the robust feedback set stabilization of switched logic control networks (SLCNs) with state-dependent uncertain switching and...
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Geometry of Deep Neural Networks
In this chapter, which is mathematically intensive, we will try to answer perhaps the most important questions of machine learning: what does the...
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The Polytope of Schedules of Processing of Identical Requirements: The Properties of the Relaxation Polyhedron
The paper deals with a set of identical requirements processing schedules for parallel machines. The precedence constraints are set, interrupts are...
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Power-Aware Analog to Digital Converters
Analog to digital converters (ADCs) enable the acquisition of analog signals by representing them in a digital format. Although the architectures and...
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Machine Learning Technology and Its Current Implementation in Agriculture
Humans have always been intrigued by the notion that a machine could simulate their brain and mimic their actions. For that reason, through the last...
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Tropical Logistic Regression Model on Space of Phylogenetic Trees
Classification of gene trees is an important task both in the analysis of multi-locus phylogenetic data, and assessment of the convergence of Markov...
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An Evidential Neural Network Model for Regression Based on Random Fuzzy Numbers
We introduce a distance-based neural network model for regression, in which prediction uncertainty is quantified by a belief function on the real...
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Basics of Numerical Computation
There was a time when computers were people [110]. But since the late 1950s with the arrival and development of electronic digital computers, people...
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Identification of model uncertainty via optimal design of experiments applied to a mechanical press
In engineering applications almost all processes are described with the help of models. Especially forming machines heavily rely on mathematical...
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An accelerated minimax algorithm for convex-concave saddle point problems with nonsmooth coupling function
In this work we aim to solve a convex-concave saddle point problem, where the convex-concave coupling function is smooth in one variable and...
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Feature Engineering with Regularity Structures
We investigate the use of models from the theory of regularity structures as features in machine learning tasks. A model is a polynomial function of...
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Application and comparison of kernel functions for linear parameter varying model approximation of nonlinear systems
In this paper, a comparative study for kernel-PCA based linear parameter varying (LPV) model approximation of sufficiently nonlinear and reasonably...
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Geometric Algebra Algorithm Code Optimised by GAALOP Executing on a Simulated Memristor Crossbar Array
GAALOP (Geometric Algebra ALgorithms OPtimizer) is software designed to optimize the execution of multiple steps in a Geometric Algebra algorithm by...
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Churn Prediction Using Mathematical Programming via a Linear Classification Algorithm
AbstractThe initial idea in develo** customer churn prediction was to use statistical analysis of a sample of previous customers to help operators...
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Relational Graph Attention-Based Deep Reinforcement Learning: An Application to Flexible Job Shop Scheduling with Sequence-Dependent Setup Times
This paper tackles a manufacturing scheduling problem using an Edge Guided Relational Graph Attention-based Deep Reinforcement Learning approach....
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Hardware Acceleration of SVM Training for Real-Time Embedded Systems: Overview
Support vector machines (SVMs) have proven to yield high accuracy and have been used widespread in recent years. However, the standard versions of...
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Deep learning theory of distribution regression with CNNs
We establish a deep learning theory for distribution regression with deep convolutional neural networks (DCNNs). Deep learning based on structured...