Pub Date : 2020-04-01DOI: 10.1142/9781786348210_0010
J. D. Martín-Guerrero, L. Lamata
Machine Learning (ML) is becoming a more and more popular field of knowledge, being a term known not only in the academic field due to its successful applications to many real-world problems. The advent of Deep Learning and Big Data in the last decade has contributed to make it even more popular. Many companies, both large ones and SMEs, have created specific departments for ML and data analysis, being in fact their main activity in many cases. This current exploitation of ML should not mislead us; while it is a mature field of knowledge, there is still room for many novel contributions, namely, a better understanding of the underlying Mathematics, proposal and tuning of algorithms suitable for new problems (e.g., Natural Language Processing), automation and optimization of the search of parameters, etc. Within this framework of new contributions to ML, Quantum Machine Learning (QML) has emerged strongly lately, speeding up ML calculations and providing alternative representations to existing approaches. This special session includes six high-quality papers dealing with some of the most relevant aspects of QML, including analysis of learning in quantum computing and quantum annealers, quantum versions of classical ML models –like neural networks or learning vector quantization–, and quantum learning approaches for measurement and control.
{"title":"Quantum Machine Learning","authors":"J. D. Martín-Guerrero, L. Lamata","doi":"10.1142/9781786348210_0010","DOIUrl":"https://doi.org/10.1142/9781786348210_0010","url":null,"abstract":"Machine Learning (ML) is becoming a more and more popular field of knowledge, being a term known not only in the academic field due to its successful applications to many real-world problems. The advent of Deep Learning and Big Data in the last decade has contributed to make it even more popular. Many companies, both large ones and SMEs, have created specific departments for ML and data analysis, being in fact their main activity in many cases. This current exploitation of ML should not mislead us; while it is a mature field of knowledge, there is still room for many novel contributions, namely, a better understanding of the underlying Mathematics, proposal and tuning of algorithms suitable for new problems (e.g., Natural Language Processing), automation and optimization of the search of parameters, etc. Within this framework of new contributions to ML, Quantum Machine Learning (QML) has emerged strongly lately, speeding up ML calculations and providing alternative representations to existing approaches. This special session includes six high-quality papers dealing with some of the most relevant aspects of QML, including analysis of learning in quantum computing and quantum annealers, quantum versions of classical ML models –like neural networks or learning vector quantization–, and quantum learning approaches for measurement and control.","PeriodicalId":398224,"journal":{"name":"The European Symposium on Artificial Neural Networks","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128848083","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2014-09-15DOI: 10.1007/978-3-319-11179-7_60
K. Dias, T. Windeatt
{"title":"Dynamic ensemble selection and instantaneous pruning for regression","authors":"K. Dias, T. Windeatt","doi":"10.1007/978-3-319-11179-7_60","DOIUrl":"https://doi.org/10.1007/978-3-319-11179-7_60","url":null,"abstract":"","PeriodicalId":398224,"journal":{"name":"The European Symposium on Artificial Neural Networks","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121426897","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2002-04-24DOI: 10.1007/1-84628-118-0_12
J. Koetsier, D. MacDonald, D. Charles, C. Fyfe
{"title":"Exploratory Correlation Analysis","authors":"J. Koetsier, D. MacDonald, D. Charles, C. Fyfe","doi":"10.1007/1-84628-118-0_12","DOIUrl":"https://doi.org/10.1007/1-84628-118-0_12","url":null,"abstract":"","PeriodicalId":398224,"journal":{"name":"The European Symposium on Artificial Neural Networks","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125428213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2000-05-01DOI: 10.1142/S0218001400000222
R. Brouwer
The main contribution of this paper is the development of an Integer Recurrent Artificial Neural Network (IRANN) for classification of feature vectors. The network consists both of threshold units or perceptrons and of counters, which are non-threshold units with binary input and integer output. Input and output of the network consists of vectors of natural numbers that may be used to represent feature vectors. For classification purposes, representatives of sets are stored by calculating a connection matrix such that all the elements in a training set are attracted to members of the same training set. The class of its attractor then classifies an arbitrary element if the attractor is a member of one of the original training sets. The network is successfully applied to the classification of sugar diabetes data, credit application data, and the iris data set.
{"title":"An integer recurrent artificial neural network for classifying feature vectors","authors":"R. Brouwer","doi":"10.1142/S0218001400000222","DOIUrl":"https://doi.org/10.1142/S0218001400000222","url":null,"abstract":"The main contribution of this paper is the development of an Integer Recurrent Artificial Neural Network (IRANN) for classification of feature vectors. The network consists both of threshold units or perceptrons and of counters, which are non-threshold units with binary input and integer output. Input and output of the network consists of vectors of natural numbers that may be used to represent feature vectors. For classification purposes, representatives of sets are stored by calculating a connection matrix such that all the elements in a training set are attracted to members of the same training set. The class of its attractor then classifies an arbitrary element if the attractor is a member of one of the original training sets. The network is successfully applied to the classification of sugar diabetes data, credit application data, and the iris data set.","PeriodicalId":398224,"journal":{"name":"The European Symposium on Artificial Neural Networks","volume":"167 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132052151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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