Papers

Quantum Machine Learning

Machine Learning Toolbox

Abstract This chapter discusses the relationship between machine learning (ML) and coloniality, which refers to enduring colonial-like power imbalances between the Global North and Global South that continue beyond the official end of colonialism. First, it explore

Introduction: Optimization and Machine Learning

Applications of Machine Learning: Signal Processing

Machine Learning: The Ghost in the Learning Machine

Abstract Turing’s best known work is concerned with whether universal machines can decide the truth value of arbitrary logic formulae. However, in this paper it is shown that there is a direct evolution in Turing’s ideas from his earlier investigations of comput

Introduction to Behavior and Machine Learning

Machine Learning with OBIEE

Research on Diabetes Prediction Based on Machine Learning

Learning Automata from Ordered Examples

Data clustering using unsupervised machine learning

Supervised Machine Learning: Classification

Indexing, Elaboration and Refinement: Incremental Learning of Explanatory Cases

Extremely randomised trees machine learning model for electricity theft detection

Statistical Methods for Analyzing Speedup Learning Experiments

ABSTRACT: As the scale of mining projects increase, rock engineers now face the task of analyzing vast amounts of data collected through traditional methods (boreholes) and newer methods (remote sensing). Machine learning (ML) has emerged as a tool that can as

Open-Source Software for Online Machine Learning

Machine Learning for Multi-stage Selection of Numerical Methods

Unsupervised machine learning

Machine learning techniques for fractured media

Machine Learning for Smart Health Care

Balancing Machine and Human Learning in Translation

Introduction to machine learning

Enhancing the pattern recognition capacity of machine learning techniques: The importance of feature positioning

Feature importance analysis of optimized machine learning modeling for predicting customers satisfaction at the United States Airlines

2. Topographic representation for quantum machine learning

Machine Learning Experiment Management With MLFlow

Supervised machine learning

A machine learning model pipeline for detecting wet pavement condition from live scenes of traffic cameras

Regularized Regression – Taming Complexity in Machine Learning

Network pharmacology is a growing area in drug discovery research that applies systems biology with network-based analysis to identify drug actions and relationships with multiple targets. Network pharmacology has transformed the traditional “one-target one-drug” paradigm to a hi

Mathematical Foundations and Machine Learning

Inferential Theory of Learning as a Conceptual Basis for Multistrategy Learning

Estimating the danger of snow avalanches with a machine learning approach using a comprehensive snow cover model

Machine Learning Classifiers

Heart sound classification using signal processing and machine learning algorithms

Spam detection for Youtube video comments using machine learning approaches

Machine Learning and Game Playing

Methods for Biomedical Data Pre-processing in Machine Learning

Recommendation of: Epigenetic profile drives accurate survival prediction in breast cancer via a multi-omics machine learning model. Round#2

Machine learning for healthcare technologies - an introduction

Research Information Security Technology Based on Machine Learning

Machine learning (ML) makes logical patterns out of various types of input data including images, texts, numbers, and any other types of data. Data derived from research will be processes through machine learning ‎algorithms and leads to a prediction that is mainly considered as

Cutting-Plane Methods in Machine Learning

This chapter introduces the fundamental concepts and principles of machine learning, serving as a theoretical foundation for the subsequent chapters of the book. It provides a comprehensive overview of the main learning paradigms, including supervised, unsupervised, semi-supervis

The manual classification of a large amount of textual materials are very costly in time and personnel. For this reason, a lot of research has been devoted to the problem of automatic classification and work on the subject dates from 1960. A lot of text classification software ha

Applications of Machine Learning

Overview of Machine Learning in Microbiome Research

Learning in the “Real World”

Applications of Machine Learning and Deep Learning in Genomics and Proteomics

Impact of Machine Learning on Translator Education

Most data scientists and machine learning practitioners focus on algorithm development and implementation. However, the proper and successful application of data science in an organisation cannot be separated from business objectives and organisational dynamics. This way of think

On-Line Learning from Search Failures

Predictive Analysis of NBA Game Outcomes through Machine Learning

Predicting classification errors using NLP-based machine learning algorithms and expert opinions

A Dichotomy Theorem for Learning Quantified Boolean Formulas

Text Sentiment Analysis for JD.com Based on Machine Learning

Conversational Agents, Natural Language Processing, and Machine Learning for Psychotherapy

Decision Revise: Epigenetic profile drives accurate survival prediction in breast cancer via a multi-omics machine learning model. Round#1

CSM: A Computational Model of Cumulative Learning

Advanced Prediction of Diabetes Onset Using Machine Learning Techniques

Lazy Learning of Bayesian Rules

Exploring the cost of equity for insurance companies in the world: evidence from machine learning approaches

Fundamentals of Robust Machine Learning

Table 8: (A) Prediction of BAI values with Machine Learning algorithm and (B) ΔNBR values with Machine Learning algorithm.

Learning Regular Languages from Simple Positive Examples

A Necessary Condition for Learning from Positive Examples

Constructing machine learning potentials with active learning

Learning DFA from Simple Examples

Amazon’s Machine Learning Toolkit: Sagemaker

Learning Boolean Functions in an Infinite Attribute Space

Integrating Guidance into Relational Reinforcement Learning

Bayesian Optimization

Current Challenges in Machine Learning

Learning with Genetic Algorithms: An Overview

Optimal Modelling of Stroke Probability Prediction Through Machine Learning

Learning to Combine Machine Translation Systems

Fourth International Conference on Machine Learning and Applications

Adversarial Machine Learning

Improved Rooftop Detection in Aerial Images with Machine Learning

Automated Diabetes Diagnosis Using Machine Learning: A Comprehensive Study

Technical Note: Q-Learning

CHILD: A First Step Towards Continual Learning

Pre-Owned Car Price Prediction Using Machine Learning Techniques

An explainable ensemble machine learning approach for multi-domain, multiclass sentiment analysis in Amazon product reviews

Ambitions and Goals of Machine Learning

Machine Learning for Semiconductor Devices

Advanced Machine Learning Approaches for Accurate Flight Delay Prediction

Generative machine learning models for image synthesis: Advances, challenges, and future directions

Car Price Prediction Based on Multiple Machine Learning Models

Robust Optimization in Machine Learning

In our research, we use sentiment analysis to determine how well ratings and reviews are compared on Amazon.com. The process of determining whether a text's tone is favorable or negative and labelling it as such is known as sentiment analysis. Consumers may write evaluations on e

Applications of Machine Learning: Robotics

In dealing with information it often turns out that one has to face a huge amount of data, often not completely homogeneous and often without an immediate grasp of an underlying simple structure. Many records, each one instantiating many variables, are usually collected with the

The General Utility Problem in Machine Learning

This video describes how to combine machine learning with classical physics models to correct for discrepancies in the data (e.g., from nonlinear friction, wind resistance, etc.). Several examples are covered, from modern robotics, to classical connections with Galileo v. Aristot

Emerging Trends in Machine Learning

The Pros and Cons of Machine Learning

Machine learning introduction

Basics of Machine Learning

Machine learning algorithms for fraud prediction in property insurance: Empirical evidence using real-world microdata

Machine Learning for Text: An Introduction

Phase Transitions in Machine Learning

Machine Learning Based Short Video Comment Count Prediction

Support Vector Machine

Fourier Neural Network for machine learning

Optimization for Machine Learning

Machine Learning and Data Analysis

Unsupervised Learning by Probabilistic Latent Semantic Analysis

Gradient-Based Learning

Review of machine learning applications for defect detection in composite materials

Information and Communication-Based Collaborative Learning and Behavior Modeling Using Machine Learning Algorithm

Boolean Feature Discovery in Empirical Learning

Explanation-Based Learning: An Alternative View

Learning Fallible Deterministic Finite Automata

Explanation-Based Learning for Diagnosis

The Practical Concepts of Machine Learning

Machine learning in cybersecurity

Interpreting Machine Learning Results

Transactional Machine Learning

Book Review: C4.5: Programs for Machine Learning by J. Ross Quinlan. Morgan Kaufmann Publishers, Inc., 1993

Machine Learning Attack Models

Texas rural land market integration: A causal analysis using machine learning applications

SSH Application Classification Based on Machine Learning

Machine Learning in Structural Biology: Interpreting 3D Protein Images

Correction to: Machine Learning for Text

An Analytic Center Machine

Open Source Machine Learning Libraries

Martin Hilbert Discusses Recommender Systems that Use Content-Based Filtering for Machine Learning

Machine Learning-Based Cloud Computing Compliance Process Automation

Machine Learning for Industrial Applications

Predicting customer purpose of travel in a low-cost travel environment—A Machine Learning Approach

Predicting firm performance and size using machine learning with a Bayesian perspective

Chronic kidney disease (CKD) is a prevalent and debilitating condition worldwide, characterized by progressive loss of kidney function over time. Early detection plays a crucial role in mitigating its impact on patient health and healthcare systems. In recent years, there has bee

Introduction: The Challenge of Reinforcement Learning

Machine learning algorithms for event detection

Machine-learning models for spatially-explicit forecasting of future racial segregation in US cities

Associative Reinforcement Learning: Functions in k-DNF

Learning Syntax by Automata Induction

Machine learning experiments

Solving Feature Selection Problems Built on Population‐based Metaheuristic Algorithms

Machine Learning Application Areas

Machine Learning Theory and Practices

XIMA: the in-ReRAM machine learning architecture

Ethical and Legal Considerations for Machine Learning and Deep Learning in Biomedical Engineering

Deep Learning: A New Approach to Machine Learning

Autonomous Driving Architectures: Insights of Machine Learning and Deep Learning Algorithms

Deep Learning Machine Translation

Increasing trust and fairness in machine learning applications within the mortgage industry

Machine Learning: From Concepts to Practice

Exact Learning of Formulas in Parallel

Biometrics aims at reliable and robust identification of humans from their personal traits, mainly for security and authentication purposes, but also for identifying and tracking the users of smarter applications. Frequently considered modalities are fingerprint, face, iris, palm

Machine learning for labor market matching

A Learning Criterion for Stochastic Rules

Introduction: Special Issue on Computational Learning Theory

This chapter looks at many prediction problems in asset pricing that are of high-dimensional nature in a large number of observable variables that could have useful predictive information. For example, in stock return prediction a huge number of variables could potentially be rel

Leveraging the momentum effect in machine learning-based cryptocurrency trading

Predictions of adsorption energies of methane-related species on Cu-based alloys through machine learning

Tabular machine learning using conjunctive threshold neural networks

Intrusion Detection Systems based on Machine Learning

This research focuses on the advancements in car detection techniques, particularly targeting wrong-way driving vehicles, using deep learning and machine learning methodologies. In recent years, numerous techniques have been proposed to address vehicle detection in real-time scen

In bioinformatics, the classification of gene-disease associations is crucial. It directly affects whether we can untangle the genetic roots of various disease as well as if we will find some justifiable therapy for these cured diseases.Using XBNet to construct genetic algorithms

Machine Learning in Tourism

Risk-Sensitive Reinforcement Learning

Machine Learning for DUNE Supernova Trigger

Industrial Applications of Machine Learning

Machine Learning Examples in MATLAB

Bayesian Landmark Learning for Mobile Robot Localization

Machine Learning in Educational Technology

Ethics in Machine Learning

The background of machine learning algorithms

The Machine‐Learning Approach

Implementing Machine Learning Algorithms

Implementation of machine learning technologies in construction maintenance: A strategic analysis

Practical Issues in Temporal Difference Learning

Fundamental Machine Learning Methods

Fundamentals of Machine Learning

Appendix E: R for Machine Learning

High-quality academic publishing is built on rigorous peer review [...]

Asynchronous Stochastic Approximation and Q-Learning

Predictive modeling in the automotive industry often involves analyzing customer behavior to anticipate events such as vehicle purchases, service visits, or campaign responses. However, when working with imbalanced data—such as rare events like luxury vehicle purchases or high-ti

Emotions play a primary role in shaping human intelligence. On the other side, for machines, it is impossible to have forms of knowledge that are not programmed, managed, or piloted by humans. Furthermore, for humans, the process of learning is deeply interwoven with the emotiona

Machine learning based risk analysis and predictive modeling of structure fire related casualties

EmoFusion: An integrated machine learning model leveraging embeddings and lexicons to improve textual emotion classification

Explaining customer churn prediction in telecom industry using tabular machine learning models

Learning with Maximum-Entropy Distributions

Encouraging Experimental Results on Learning CNF

Shifting Vocabulary Bias in Speedup Learning

Task-Structures, Knowledge Acquisition and Learning

Machine Learning Methods for Managing Parkinson’s Disease

Introducing the Special Issue of Machine Learning Selected from Papers Presented at the 1997 Conference on Computational Learning Theory, COLT '97

Robust Learning with Missing Data

Machine Learning Building Blocks

Editorial: Launching <i>Machine Learning: Earth</i>—a new forum for machine learning in the Earth sciences

Learning to Recognize and Grasp Objects

Machine Learning Based News Text Classification

Chapter 5 Introduction to Machine Learning

A Statistical Machine Learning Framework

A Simple Machine-Learning Task

AI and Machine Learning Capstone Project

Machine learning (ML)

What Is Machine Learning?

This paper analyzes a dataset containing radio frequency (RF) measurements and Key Performance Indicators (KPIs) captured at 1876.6MHz with a bandwidth of 10MHz from an operational 4G LTE network in Nigeria. The dataset includes metrics such as RSRP (Reference Signal Received Pow

Machine Learning for IT Security

A survey of machine learning in kidney disease diagnosis

Intrusion Detection with Neural Networks: A Tutorial

Learning to Predict Non-Deterministically Generated Strings

Kernel-Based Reinforcement Learning

Machine learning wavefunction

APL Machine Learning reviewer acknowledgment for 2024

Machine learning approaches to traffic accident severity prediction: Addressing class imbalance

Technology Application of Autonomous Vehicle in Machine Learning

Machine Discovery of Effective Admissible Heuristics

Machine learning-driven predictive modeling of temperature-dependent mechanical properties in austenitic stainless steels

Improving mango ripeness grading accuracy: A comprehensive analysis of deep learning, traditional machine learning, and transfer learning techniques

4. Intro to Machine Learning

What Is Machine Learning Forensics?

Machine learning theory

An Introduction to MCMC for Machine Learning

Classical Machine-Learning Paradigms for Data Mining

Introduction to Machine Learning, Deep Learning, and Natural Language Processing

Higgs event classification using Machine Learning

Estimation of Groundwater Levels Using Machine Learning Techniques

Using Azure Machine Learning Studio

Federated Machine Learning

Let’s Integrate with Machine Learning

Martin Hilbert Discusses Recommender Systems that Use Collaborative Filtering for Machine Learning

Linear Algebra for Machine Learning

Chapter 6 Classifiers in Machine Learning

Semisupervised Learning for Machine Translation

MLlib: Machine Learning Library

An important diagnostic method for diagnosing abnormalities in the human heart is the electrocardiogram (ECG). A large number of heart patients increase the assignment of physicians. To reduce their assignment, an automatic computer detection system is needed. In this study, a co

Forecasting Slugging Using Machine Learning

NBA Player Score Prediction Based on Machine Learning

Learning from Different Teachers

Quantum Machine Learning (QML) is an emerging interdisciplinary field that combines the principles of quantum mechanics and machine learning to develop algorithms that can potentially outperform classical algorithms in certain tasks. QML leverages the unique properties of quantum

Learning from Cluster Examples

Machine Learning Applications

Machine Learning in Metabolic Engineering

Overview of Machine Learning

Predictors of COVID-19 vaccination rate in USA: A machine learning approach

Reinforcement learning for control of valves

Machine learning basics

Machine Learning Microbiome with SIAMCAT

Combining Symbolic and Neural Learning

Machine Learning Model Evaluation

Understanding Machine Learning

Learning From Noisy Examples

Introduction: Cognitive Autonomy in Machine Discovery

Machine Learning in the Cloud

On Learning Sets and Functions

Disease Detection System (DDS) Using Machine Learning Technique

Applications of Machine Learning and Deep Learning

Predicting severely imbalanced data disk drive failures with machine learning models

Harnessing machine learning for decoding Caenorhabditis elegans behavior

Interpretable trading pattern designed for machine learning applications

Teachable Machine как стартовая точка в Machine Learning

An Experimental Evaluation of Integrating Machine Learning with Knowledge Acquisition

Automating and Consuming Machine Learning

From machine learning to machine reasoning

Machine learning-based identification of craniosynostosis in newborns

Learning Conjunctive Concepts in Structural Domains

Deriving mapping functions to tie anthropometric measurements to body mass index via interpretable machine learning

Azure Machine Learning

Automated Machine Learning for Multimedia

Different Machine Learning Models

Software for Machine Learning

1. Introduction of machine learning

Erratum to “New interpretation of GNRVⓇ knee arthrometer results for ACL injury diagnosis support using machine learning” [Machine Learning with Applications 13 (2023) 100480]

Parallelized hybrid ensemble machine learning framework for scalable and accurate rainfall prediction

1. Lie group machine learning model

Adaptive Versus Nonadaptive Attribute-Efficient Learning

Machine Learning for Profiling Network Traffic

Summarizing AI and Machine Learning

The widespread utilization of the internet and computer systems has resulted in notable security concerns, characterized by a surge in intrusions and vulnerabilities. Malicious users manipulate internal systems, resulting in the exploitation of software flaws and default setups. 

Signal processing and machine learning theory

Tracking Context Changes through Meta-Learning

Learning at the Knowledge Level

Dermatological Machine Learning Clinical Decision Support System

Machine Learning in Healthcare

Rigorous peer-reviews are the basis of high-quality academic publishing [...]

Medicine: Applications of Machine Learning

1. Dynamic fuzzy machine learning model

Interpretable Machine Learning

Deploying Machine Learning Models

Appendix G: Machine Learning Careers

Alarming Large Scale of Flight Delays: an Application of Machine Learning

Research at the intersection of machine learning (ML) and quantum physics is a recent growing field due to the enormous expectations and the success of both fields. ML is arguably one of the most promising technologies that has and will continue to disrupt many aspects of our liv

Editorial: Welcome to APL Machine Learning

Traditional Machine Learning Evaluation

Green machine learning protocols for machine-to-machine communication

Chapter 1 Introduction to Machine Learning

Comparative Insights into Machine Learning Training Techniques

Machine Learning in Sports 101

Identifying Diseases and Diagnosis Using Machine Learning

Tools for fully data-driven machine learning

An Overview of Machine Learning Methods

Machine Learning for Plant Biology

Genetics-Based Machine Learning