Temat: nearest neighbour - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: A strategy in sports betting with the nearest neighbours search and genetic algorithms
Autorzy:: Borycki, D.
Powiązania:: https://bibliotekanauki.pl/articles/106184.pdf
Data publikacji:: 2011
Wydawca:: Uniwersytet Marii Curie-Skłodowskiej. Wydawnictwo Uniwersytetu Marii Curie-Skłodowskiej
Tematy:: sports betting
nearest neighbour search
genetic algorithm
English Premier League
Opis:: The point of sports betting is not merely to correctly predict the outcome of a game, but to actually win on a bet. We propose a model of sports betting that uses the nearest neighbours search and genetic algorithms to do the job. It uses data on the teams playing, their respective formations, individual players, results of previous games, as well as odds offered by bookmakers. The model has been trained using the data from the seasons 2002/03 until 2008/09 of the English Premier League and tested against the already played games of the seasons 2009/10 and 2010/11.
Źródło:: Annales Universitatis Mariae Curie-Skłodowska. Sectio AI, Informatica; 2011, 11, 1; 7-13
1732-1360
2083-3628
Pojawia się w:: Annales Universitatis Mariae Curie-Skłodowska. Sectio AI, Informatica
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Prediction of mortality rates in heart failure patients with data mining methods
Autorzy:: Bohacik, J.
Kambhampati, C.
Davis, D. N.
Cleland, J. G. F.
Powiązania:: https://bibliotekanauki.pl/articles/908867.pdf
Data publikacji:: 2013
Wydawca:: Uniwersytet Marii Curie-Skłodowskiej. Wydawnictwo Uniwersytetu Marii Curie-Skłodowskiej
Tematy:: heart failure
data mining
prediction of mortality rates
home telemonitoring
Bayesian network method
decision tree method
neural network method
nearest neighbour method
Opis:: Heart failure is one of the severe diseases which menace the human health and affect millions of people. Half of all patients diagnosed with heart failure die within four years. For the purpose of avoiding life-threatening situations and minimizing the costs, it is important to predict mortality rates of heart failure patients. As part of a HEIF-5 project, a data mining study was conducted aiming specifically at extracting new knowledge from a group of patients suffering from heart failure and using it for prediction of mortality rates. The methodology of knowledge discovery in databases is analyzed within the framework of home telemonitoring. Several data mining methods such as a Bayesian network method, a decision tree method, a neural network method and a nearest neighbour method are employed. The accuracy for the data mining methods from the point of view of avoiding life-threatening situations and minimizing the costs is discussed. It seems that the decision tree method achieves the best accuracy results and is also interpretable for the clinicians.
Źródło:: Annales Universitatis Mariae Curie-Skłodowska. Sectio AI, Informatica; 2013, 13, 1; 7-16
1732-1360
2083-3628
Pojawia się w:: Annales Universitatis Mariae Curie-Skłodowska. Sectio AI, Informatica
Dostawca treści:: Biblioteka Nauki

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