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2017, Oxford handbooks, ISBN 0199372136, xxx, 700 pages
"Few aspects of daily existence are untouched by technology. Learning and teaching music are no exceptions and arguably have been impacted as much or more than other areas of life... 
Music | Technological innovations | Instruction and study | Digital citizenship | Educational change | Music literacy | Creative practice | Educational technologies | Evidence-based education | Preservice teachers | Technology skill development | Participatory culture | Sound and place | Initial teacher education | Deterritorialization | Adaptive technology | Distinction | Learners with exceptionalities | Makers | Music learning | Music education curriculum | Africa | Techno-human future | Praxis shock | Long-distance learning | Musical subjectivity | Pop | Twenty-first-century skills | Instrument-making | Universal design for learning | Romanticism | Technological determinism | Finland | Stakeholders | Teacher preparation | Music teacher education | Learning and teaching | Music teacher roles | Problem-finding | Authority | Musicians workshop | Disabilities | Slow music | Collaborative model | School classrooms | Contextual | Innovation | Faculty development | Literacies | Innovative pedagogies | Professional development | Communication | Context | Multimedia | Social | Skill development | Techné | Public health communication | Self-determination | Social technologies | Musically educated | Education technology | Irrelevant | West africa | Out-of-school learning | Curriculum | Place | Power | Participation | Enabling technologies | Humans | Music notation | Totally pedagogized society | European perspectives | Living well with less | Learner-centered | Metaphor | Multiarts | Teaching | Learning | Creative literacy | Networked technologies | Heidegger | Pedagogical fundamentalism | Peer-to-peer learning | Technology and music creativity | Sound recordings | Teenagers | Digital technology | Primary schools | Creation | Technological affordances | Interaction | Behavior change | Parochial practice | English secondary schools | Music teacher preparation | Music technologies | Democratic education | Scepticism | Music technology | Digital audio workstations | Maker space | Technology integration | Technological limitations | Tpack | Mobile learning | Information and communication technology | Internet | Savoring | Teacher attitudes | Practice-led enquiry | Greek education | Postsecondary music education | Teacher resistance | Inclusion | Epistemology | Hip-hop | Administrative technologies | Equity | Risk | Narcissism | Higher education change | Poiesis | Classroom music teaching | Classroom | Community music | Digital pedagogy | Technology traditions | Authentic processes | Autonomy | Children | Music education technology | Popular music | Aristotle | International | Non-formal music education | Composition | Health | Globalization | Pop music | Rhythmic video games | Exploration | Motivate | Learner/musician | Apprenticeship | Higher education | Slow food | Informal learning | Community | Gender issues | Music fluency | Education policy | Autonomy popular music | Credentialing | Education 3.0 | Communities of musical practice | Music education | Prosumer | Technology access | School-based routes | Pre-service teachers | Ict in music education | Musical intelligence | Mediatization | Augmenting music | Local | Disease control | Creativity | Technologies | Technology competencies | Local context | Totally technologized society | Teacher educator | Teaching with technology | Performance | Sociocultural | Digital natives | Texas music education | Guinea | Diversity | Teacher certification | Invention | Ensembles | Implementation | Empowerment | Technogenesis | Well-being | Music teachers’ concerns | Sociology | Musical analysis | Informal music learning | Digital literacies | Pedagogy in-service preservice | Sequencing | Information communications technology | Communities of response | Conservatory model | Tools | Spreadability | Flow | Learning resources | African context | Interest-driven learning | Play | School experience | Pedagogy | Recording | Differentiation | Integration of technology | Policy | Choice | Digital arts | Music teacher certification | Limitations | Passeur culturel | Celebrities | Radical pedagogy | Education | Ofsted | Technology | Learner agency | Electronica | Culture | Musicking | Role of technology | Pedagogical paradigms | Teacher training | Digital culture | Ethnomusicology | Youth | Vulnerability | Pressure | Mobile technologies | Place philosophy | Digital media | Piano | Groove | School music | Internet evolution | Software | Musicianship | Technology use philosophies of technology | Assimilation | Facilitator | Constructionism | New media | At-risk student | Self led development | Music teacher educators | Rhythmic video games music technology | Notation | Music making | Perspective | Music experience | Higher music education | Participatory music | East timor | Music theory | Triple revolution | Portfolios | Ethnography | Futurism | Gcse examination | Music teacher attitudes | West | Fear of change | Digital music | Opportunities | Change | Commodification | Linguistic relativity | Digital instruments | Technological assimilation | Digital learners | Inclusive | Pluralism | Collaboration | Teacher education
Book
IEEE Transactions on Fuzzy Systems, ISSN 1063-6706, 7/2019, pp. 1 - 1
There have been different strategies to improve the performance of a machine learning model, e.g... 
Training | patch learning | Computational modeling | Two dimensional displays | Training data | regression | Machine learning | fuzzy system | Data models | Ensemble learning | Fuzzy systems
Journal Article
IEEE Intelligent Systems, ISSN 1541-1672, 11/2013, Volume 28, Issue 6, pp. 30 - 59
Journal Article
Journal Article
Pattern Recognition, ISSN 0031-3203, 12/2016, Volume 60, pp. 692 - 705
... more powerful learning models. For example, the AdaBoost approach only investigates the data sample space, while the random subspace technique only focuses on the feature space... 
Random subspace | Classifier ensemble | AdaBoost | Ensemble learning | Decision tree | SUPPORT VECTOR MACHINES | RANDOM FORESTS | CLASSIFIER | DATA-SETS | FUZZY | NETWORKS | COMBINATION | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | ENGINEERING, ELECTRICAL & ELECTRONIC | FRAMEWORK | SYSTEMS | SELECTION | Computer science | Analysis
Journal Article
Decision Support Systems, ISSN 0167-9236, 12/2014, Volume 68, pp. 26 - 38
.... In a decision making context, one of the most relevant tasks is polarity classification of a text source, which is usually performed through supervised learning methods... 
Ensemble learning | Sentiment analysis | Polarity classification | COMPUTER SCIENCE, INFORMATION SYSTEMS | OPERATIONS RESEARCH & MANAGEMENT SCIENCE | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | Learning | Uncertainty | Tasks | Classification | Texts | Polarity | Mathematical models | Bayesian analysis
Journal Article
Mechanical Systems and Signal Processing, ISSN 0888-3270, 01/2019, Volume 115, pp. 213 - 237
•We conduct a detailed review of the applications of recent deep learning models on machine health monitoring tasks and provide our own insights into these models... 
Deep learning | Big data | Machine health monitoring | BEARING FAULT-DIAGNOSIS | ENSEMBLE | CONVOLUTIONAL NEURAL-NETWORK | FEATURE-EXTRACTION | ROTATING MACHINERY | CLASSIFICATION | MODEL | ENGINEERING, MECHANICAL | AUTOENCODER | PREDICTION | ROBUST IDENTIFICATION | Magneto-electric machines | Image processing | Neural networks | Machinery | Machine learning
Journal Article
Expert Systems With Applications, ISSN 0957-4174, 2009, Volume 36, Issue 10, pp. 11994 - 12000
Journal Article
Information Fusion, ISSN 1566-2535, 09/2017, Volume 37, pp. 132 - 156
....•Discussion of open research problems and lines of future research. In many applications of information systems learning algorithms have to act in dynamic environments where data are collected in the form of transient data streams... 
Non-stationary environments | Data streams | Ensemble learning | Concept drift | Online learning | REGRESSION | CLASSIFIER | NOVELTY DETECTION | ALGORITHM | IMBALANCE | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | WEIGHTED MAJORITY | TREES | FRAMEWORK | COMPUTER SCIENCE, THEORY & METHODS | SELECTION | Computer science | Surveys | Algorithms | Artificial intelligence | Data mining
Journal Article