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2012, SEMPRE studies in the psychology of music, ISBN 9781409447658, 242
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., increasing the depth, width, and/or nonlinearity of the... 
Training | patch learning | Computational modeling | Two dimensional displays | Training data | regression | Machine learning | fuzzy system | Data models | Ensemble learning | Fuzzy systems
Journal Article
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... 
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 Intelligent Systems, ISSN 1541-1672, 11/2013, Volume 28, Issue 6, pp. 30 - 59
Journal Article
Journal Article
IEEE Transactions on Power Systems, ISSN 0885-8950, 01/2014, Volume 29, Issue 1, pp. 359 - 367
Journal Article
Information Sciences, ISSN 0020-0255, 2012, Volume 185, Issue 1, pp. 66 - 77
Journal Article
International Journal of Machine Learning and Cybernetics, ISSN 1868-8071, 6/2011, Volume 2, Issue 2, pp. 107 - 122
Journal Article
2016, New edition, SEMPRE studies in the psychology of music, ISBN 1472439570, xvi, 165
In the age of digital music it seems striking that so many of us still want to produce music concretely with our bodies, through the movement of our limbs,... 
Musical instruments | Psychological aspects | Physiological aspects | Performance | Music | Instruction and study | Musical Instruments & Ensembles | Psychology of Music | Music & Education | Ethnomusicology
Book
Pattern Recognition, ISSN 0031-3203, 12/2016, Volume 60, pp. 692 - 705
There are not many classifier ensemble approaches which investigate the data sample space and the feature space at the same time, and this multi-pronged... 
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
The huge amount of textual data on the Web has grown in the last few years rapidly creating unique contents of massive dimension. In a decision making context,... 
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
IEEE Transactions on Neural Networks and Learning Systems, ISSN 2162-237X, 11/2018, Volume 29, Issue 11, pp. 5366 - 5379
Journal Article
IEEE Transactions on Knowledge and Data Engineering, ISSN 1041-4347, 08/2019, Volume 31, Issue 8, pp. 1506 - 1519
Traditional learning from crowdsourced labeled data consists of two stages: inferring true labels for instances from their multiple noisy labels and building a... 
Crowdsourcing | Training | Learning systems | ensemble learning | Inference algorithms | Data models | learning from crowds | Noise measurement | Labeling | classification | Bagging | crowdsourcing | DECISION TREES | COMPUTER SCIENCE, INFORMATION SYSTEMS | NOISE | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | ENGINEERING, ELECTRICAL & ELECTRONIC
Journal Article
by Nan Liu and Han Wang
IEEE Signal Processing Letters, ISSN 1070-9908, 08/2010, Volume 17, Issue 8, pp. 754 - 757
Journal Article
Expert Systems With Applications, ISSN 0957-4174, 10/2017, Volume 83, pp. 405 - 417
Journal Article