About: Some Studies in Machine Learning Using the Game of Checkers / Arthur L. Samuel.

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rdf:type	Item
description	article (en) ലേഖനം (ml) wetenschappelijk artikel (nl) наукова стаття, опублікована в липні 1959 (uk) Artikel aus "IBM Journal of Research and Development" veröffentlicht im Juli 1959 (de)
publication date	wds:Q55877812-366E8404-65D9-4453-993C-7975647E285E
publication date	1959-07-01 00:00:00Z (xsd:dateTime)
author name string	wds:Q55877812-7A4A7874-BA91-4E74-86A5-3623092E193F
author name string	A. L. Samuel
rdfs:label	Some Studies in Machine Learning Using the Game of Checkers (nl) Some Studies in Machine Learning Using the Game of Checkers / Arthur L. Samuel. - (1959) (de) Some Studies in Machine Learning Using the Game of Checkers / Arthur L. Samuel. - (1959) (en)
skos:prefLabel	Some Studies in Machine Learning Using the Game of Checkers (nl) Some Studies in Machine Learning Using the Game of Checkers / Arthur L. Samuel. - (1959) (de) Some Studies in Machine Learning Using the Game of Checkers / Arthur L. Samuel. - (1959) (en)
name	Some Studies in Machine Learning Using the Game of Checkers (nl) Some Studies in Machine Learning Using the Game of Checkers / Arthur L. Samuel. - (1959) (de) Some Studies in Machine Learning Using the Game of Checkers / Arthur L. Samuel. - (1959) (en)
author	wds:Q55877812-e68eda15-4214-0cf3-1894-864b77871353
author	Arthur Samuel
title	wds:Q55877812-2E1A59A9-7BC7-4E39-95ED-F9E78CAAB70A
title	Some Studies in Machine Learning Using the Game of Checkers (en)
page(s)	wds:Q55877812-8444B57A-4FFF-482B-857B-5FF3B3E08E05
page(s)	210-229
instance of	wds:Q55877812-11B3D50D-2A22-458B-8B60-59A730D18D7C
instance of	scholarly article
main subject	wds:Q55877812-E2C9C26A-CB21-421E-BEA5-038C4B381DDA
main subject	machine learning
DBLP publication ID	wds:Q55877812-7577E52C-48DB-4324-A0A2-2A363D11D52A
DBLP publication ID	https://dblp.org/rec/journals/ibmrd/Samuel59
DBLP publication ID	journals/ibmrd/Samuel59
published in	wds:Q55877812-77D60932-C450-4994-AAD6-7E2D6E9ACE03
published in	IBM Journal of Research and Development
issue	wds:Q55877812-29C62A93-E21A-4972-822B-E11A1D02C94D
volume	wds:Q55877812-360F0801-5D1F-4DB4-89F9-1F4235688F18
issue	3
volume	3
DOI	wds:Q55877812-C68FBEC2-818C-4DE4-AA5E-3B7A3AE41FCA
DOI	http://dx.doi.org/10.1147/RD.33.0210
DOI	10.1147/RD.33.0210
is about of	https://www.wikidata.org/wiki/Special:EntityData/Q55877812
is cites work of	Mapping value based planning and extensively trained choice in the human brain Computer Simulation of Human Thinking Elephants don't play chess Computational phenotype discovery using unsupervised feature learning over noisy, sparse, and irregular clinical data The algorithmic anatomy of model-based evaluation. Bee foraging in uncertain environments using predictive hebbian learning Microscopic-macroscopic interface in biological information processing Protein function in precision medicine: deep understanding with machine learning Interactive machine learning for health informatics: when do we need the human-in-the-loop? Cooperative coevolution: an architecture for evolving coadapted subcomponents ZCS redux. Artificial intelligence Evolution and gradualness Speculations Concerning the First Ultraintelligent Machine Programming backgammon using self-teaching neural nets Ideal evaluation from coevolution. The revolution of personalized psychiatry: will technology make it happen sooner? Machine Learning for Health Informatics Development of the Product State Concept Classical Statistics and Statistical Learning in Imaging Neuroscience Machine Learning Methods for Analysis of Metabolic Data and Metabolic Pathway Modeling. Correct machine learning on protein sequences: a peer-reviewing perspective. The impact of artificial intelligence in the diagnosis and management of glaucoma Introduction to Machine Learning in Digital Healthcare Epidemiology Artificial Intelligence / Marvin L. MInsky. - (9.1966) Big data and machine learning algorithms for health-care delivery Artificial intelligence to deep learning: machine intelligence approach for drug discovery From motor control to team play in simulated humanoid football “Memo” Functions and Machine Learning Reinforcement learning improves behaviour from evaluative feedback Striatal and Tegmental Neurons Code Critical Signals for Temporal-Difference Learning of State Value in Domestic Chicks Machine learning applications in cell image analysis A Local Learning Algorithm for Dynamic Feedforward and Recurrent Networks On the complexity of computing and learning with multiplicative neural networks. Parallel Distributed Processing at 25: further explorations in the microstructure of cognition. Computer Go: An AI oriented survey The computer and information sciences and the community of disciplines A Perspective on Wearable Sensor Measurements and Data Science for Parkinson's Disease. Generalizability of machine learning for classification of schizophrenia based on resting-state functional MRI data Tamping Ramping: Algorithmic, Implementational, and Computational Explanations of Phasic Dopamine Signals in the Accumbens Regulatory element-based prediction identifies new susceptibility regulatory variants for osteoporosis. Introduction to MAchine Learning & Knowledge Extraction (MAKE) Artificial Intelligence, Pattern Recognition, and Neurobiology Machine learning in manufacturing: advantages, challenges, and applications Metabolomics applied in the study of emerging arboviruses caused by Aedes aegypti mosquitoes: A review Interactive Machine Learning (iML) Open Problems in Universal Induction & Intelligence Computer shogi Optimizing agent behavior over long time scales by transporting value Fireball streak detection with minimal CPU processing requirements for the Desert Fireball Network data processing pipeline Machine learning and games Checkers, a game that can be more interesting than one might think / Martin Gardner. - (1.1980) Dimensionality reduction in neuroscience and Epidemiology An LP-based hyperparameter optimization model for language modeling A mathematical model for performing a complex task in a card game Game playing. Dynamical genetic programming in XCSF. Learning physical properties of liquid crystals with deep convolutional neural networks

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