Sökning: "missing data"
Visar resultat 6 - 10 av 422 avhandlingar innehållade orden missing data.
6. Methods for Analysis of Naturalistic Driving Data in Driver Behavior Research
Sammanfattning : In the last several years, the focus of traffic safety research—especially when performed in association with the automotive industry—has shifted from preventing injury during a crash to avoiding the crash altogether or mitigating its effects. Pre-crash safety measures include intelligent safety systems (e.g. LÄS MER
7. Predictive Techniques and Methods for Decision Support in Situations with Poor Data Quality
Sammanfattning : Today, decision support systems based on predictive modeling are becoming more common, since organizations often collect more data than decision makers can handle manually. Predictive models are used to find potentially valuable patterns in the data, or to predict the outcome of some event. LÄS MER
8. Interpretable machine learning models for predicting with missing values
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
9. Prostate cancer incidence, treatment and mortality : Empirical longitudinal register-based studies and methods for handling missing data
Sammanfattning : The diagnostic activity for prostate cancer has increased substantially in Sweden, primarily due to increased use of prostate-specific antigen (PSA) testing in asymptomatic men, and this has led to a large increase in diagnoses. There have also been changes in the diagnostic workup, guidelines, treatment strategies, and more effective treatments have been introduced in different phases of the disease. LÄS MER
10. Representation learning for natural language
Sammanfattning : Artificial neural networks have obtained astonishing results in a diverse number of tasks. One of the reasons for the success is their ability to learn the whole task at once (endto-end learning), including the representations for data. LÄS MER