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photo credits: Wikimedia Commons
Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions. Recently, artificial neural networks have been able to surpass many previous approaches in performance. ML finds application in many fields, including natural language processing, computer vision, speech recognition, email filtering, agriculture, and medicine. The application of ML to business problems is known as predictive analytics. Statistics and mathematical optimization (mathematical programming) methods comprise the foundations of machine learning. Data mining is a related field of study, focusing on exploratory data analysis (EDA) via unsupervised learning. From a theoretical viewpoint, probably approximately correct (PAC) learning provides a framework for describing machine learning. Source: Wikipedia (en)
Works about machine learning 73
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Statistical Learning in Multiple Instance Problems
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Reinforcement Learning for Racecar Control
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A Comparison of Multi-instance Learning Algorithms
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An evaluation system for intelligent smart badges
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Effective Linear-Time Feature Selection
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A Homogeneous Hierarchical Scripted Vector Classification Network with Optimisation by Genetic Algorithm
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Prediction Intervals for Class Probabilities
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Learning Instance Weights in Multi-Instance Learning
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Accelerating classifier training using AdaBoost within cascades of boosted ensembles
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Prediction of Oestrus in Dairy Cows: An Application of Machine Learning to Skewed Data
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Machine Learning for Adaptive Computer Game Opponents
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Sampling-based Prediction of Algorithm Runtime
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Pest risk assessment of light brown apple moth, Epiphyas postvittana (Lepidoptera: Tortricidae) using climate models and fitness-related genetic variation
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Learning Actions That Reduce Variation in Objects
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Smoothing in Probability Estimation Trees
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Using Output Codes for Two-class Classification Problems
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Machine learning: a probabilistic perspective
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Parameter Tuning Using Gaussian Processes
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Determining the accuracy of budgets : a machine learning application for budget change pattern recognition
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Machine Learning for Intelligent Control: Application of Reinforcement Learning Techniques to the Development of Flight Control Systems for Miniature UAV Rotorcraft
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Improving Query Term Expansion With Machine Learning
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Dirichlet Methods for Bayesian Source Detection in Radio Astronomy Images
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Brain-computer interfaces for virtual Quadcopters based on a spiking-neural network architecture - NeuCube
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Linear Genetic Programming with Experience
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Gaussian discrete restricted Boltzmann machine : theory and its applications
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Autonomously Learning About Meaningful Actions from Exploratory Behaviour
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Machine Learning for Argumentation Mining
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Is Biofeedback Training of Ownership Perceptions Possible? EEG Classification of Volitional Hand-Ownership using Common Spatial Patterns
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Applied statistical modelling to guide the control of introduced mammalian predators in the Murchison Mountains (Fiordland, New Zealand)
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Semantic Search for Novel Information
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Law and Ethics of Morally Significant Machines: The case for pre-emptive prevention
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Evaluating spammer detection systems for Twitter
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