Sökning: "Ensemble Learning"

Visar resultat 11 - 15 av 69 avhandlingar innehållade orden Ensemble Learning.

1. 11. Deep Learning Applications for Biomedical Data and Natural Language Processing

   Författare :Dennis Medved; Institutionen för datavetenskap; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; macine learning; natural language processing; heart transplantation; deep learning;

   Sammanfattning : The human brain can be seen as an ensemble of interconnected neurons, more or less specialized to solve different cognitive and motor tasks. In computer science, the term deep learning is often applied to signify sets of interconnected nodes, where deep means that they have several computational layers. LÄS MER

3. 12. Forecasting of Icing Related Wind Energy Production Losses : Probabilistic and Machine Learning Approaches

   Författare :Jennie Molinder; Anna Sjöblom; Heiner Körnich; Hans Bergström; Erik Nilsson; Sue Ellen Haupt; Uppsala universitet; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; Wind energy; Icing on wind turbines; Machine learning; Probabilistic forecasting; Meteorologi; Meteorology;

   Sammanfattning : Icing on wind turbine blades causes significant production losses for wind energy in cold climate. Next-day forecasts of these production losses are crucial for the power balance in the electrical grid and for the trading process, but they are uncertain due to lack of understanding of, and simplifications, in the modelling chain. LÄS MER

4. 13. Nonconformity Measures and Ensemble Strategies : An Analysis of Conformal Predictor Efficiency and Validity

   Författare :Henrik Linusson; Ulf Johansson; Henrik Boström; Alexander Gammerman; Stockholms universitet; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; Data Science; Machine Learning; Conformal Prediction; Classification; Regression; Computer and Systems Sciences; data- och systemvetenskap;

   Sammanfattning : Conformal predictors are a family of predictive models that associate with each of their predictions a measure of confidence, enabling them to provide quantitative information about their own trustworthiness. In risk-laden machine learning applications, where bad predictions may lead to economic loss, personal injury, or worse, such inherent quality control appears highly beneficial, if not required. LÄS MER

5. 14. Ensembles of Semantic Spaces : On Combining Models of Distributional Semantics with Applications in Healthcare

   Författare :Aron Henriksson; Hercules Dalianis; Martin Duneld; Nigel Collier; Stockholms universitet; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; natural language processing; machine learning; distributional semantics; ensemble learning; semantic space ensembles; medical informatics; electronic health records; Computer and Systems Sciences; data- och systemvetenskap;

   Sammanfattning : Distributional semantics allows models of linguistic meaning to be derived from observations of language use in large amounts of text. By modeling the meaning of words in semantic (vector) space on the basis of co-occurrence information, distributional semantics permits a quantitative interpretation of (relative) word meaning in an unsupervised setting, i. LÄS MER

7. 15. Order in the random forest

   Författare :Isak Karlsson; Henrik Boström; Lars Asker; Pierre Geurts; Stockholms universitet; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; Machine learning; random forest; ensemble; time series; data series; sequential data; sparse data; high-dimensional data; Computer and Systems Sciences; data- och systemvetenskap;

   Sammanfattning : In many domains, repeated measurements are systematically collected to obtain the characteristics of objects or situations that evolve over time or other logical orderings. Although the classification of such data series shares many similarities with traditional multidimensional classification, inducing accurate machine learning models using traditional algorithms are typically infeasible since the order of the values must be considered. LÄS MER