Sökning: "Missing features"

Visar resultat 1 - 5 av 58 avhandlingar innehållade orden Missing features.

1. 1. Generalization under Model Mismatch and Distributed Learning

   Författare :Martin Hellkvist; Ayca Özcelikkale; Anders Ahlén; Martin Jaggi; Uppsala universitet; []
   Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Machine learning; Signal processing; Generalization error; Training error; Double-descent; Double descent; Distributed learning; Distributed optimization; Learning over networks; Model mismatch; Model misspecification; Fake features; Missing features; linear regression; regularization; Machine learning; Maskininlärning;

   Sammanfattning : Machine learning models are typically configured by minimizing the training error over a given training dataset. On the other hand, the main objective is to obtain models that can generalize, i.e., perform well on data unseen during training. LÄS MER

3. 2. Interpretable machine learning models for predicting with missing values

   Författare :Lena Stempfle; Chalmers tekniska högskola; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; missing values; Machine learning; healthcare; interpretable machine learning;

   Sammanfattning : Machine learning models are often used in situations where model inputs are missing either during training or at the time of prediction. If missing values are not handled appropriately, they can lead to increased bias or to models that are not applicable in practice without imputing the values of the unobserved variables. LÄS MER

4. 3. Truncation and missing family links in population-based registers

   Författare :Monica Leu; Karolinska Institutet; Karolinska Institutet; []
   Nyckelord :Left-trunction; Swedish Cancer Registry; MultiGeneration Register; family history; misclassification; simulation; Poplab;

   Sammanfattning : Studies of familial aggregation of disease routinely use linked population registers to construct retrospective cohorts. Although such resources have provided numerous estimates of familial risk, little is known regarding the sensitivity of the estimates to assumed disease models and incompleteness of the data, such as truncation and/or missing family links. LÄS MER

5. 4. Computer Vision: From Motion Capture to Tabletop Interaction

   Författare :Tommaso Piazza; Chalmers tekniska högskola; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; optical tracking; tabletop; motion capture; cscw; missing maker; multi-touch;

   Sammanfattning : This thesis presents the work done by the candidate in the domain of Human-Computer Interaction. In particular, the candidate has investigated how techniques in Computer Vision can be used to enable new forms of interaction. The work investigates areas such as motion capture and multi-user, multi-touch interaction for tabletop systems. LÄS MER

7. 5. Machine Learning Techniques with Specific Application to the Early Olfactory System

   Författare :Benjamin Auffarth; Anders Lansner; Tony Lindeberg; Tim Pearce; KTH; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; feature selection; image features; pattern classification; relevance; redundancy; distributional similarity; divergence measure; genetic algorithms; clustering algorithms; annealing; olfactory coding; olfactory bulb; odorants; glomeruli; property-activity relationship; olfaction; plasticity; axonal guidance; odor category; perception; spatial coding; population coding; memory organization; odor quality; SRA - ICT; SRA - Informations- och kommunikationsteknik;

   Sammanfattning : This thesis deals with machine learning techniques for the extraction of structure and the analysis of the vertebrate olfactory pathway based on related methods. Some of its main contributions are summarized below. LÄS MER