Data Fit (Stress) vs. Model Fit (Recovery) in Multidimensional Scaling

Assessing Model Fit with Synthetic vs. Real Data

Assessing whether a model is a good fit to the data is non trivial. The standard practice is to compare a few machine learning techniques to learn a model from data, and pick the one with the highest predictive performance. The winner is considered the best fitting model. But each model may involve different machine learning algorithms that carry their own set of parameters and constraints impo...

Electroweak Data and Standard Model Fit Results

Most recent tests of the Standard Model of the electroweak interaction are reported using data from the four LEP experiments ALEPH, DELPHI, L3 and OPAL, the SLD experiment at SLC, the Tevatron p¯ p experiments CDF and D0, and the NuTeV neutrino experiment. Consistency of the Standard Model is studied. The value of the Higgs mass is inferred from a global fit. Latest precision electroweak data (...

Combining mental fit and data fit for classification rule selection

Fit Body, Fit Mind?

I N B R I E F We are used to thinking of intelligence as largely a matter of genetic inheritance, but that is not the whole picture. What we do aff ects our mental wellbeing: staying physically and mentally active helps us stay sharp as we age. Nevertheless, our personal eff orts to bolster cognitive enhancement cannot forestall all declines in our cognitive performance. What is especially surp...

Multidimensional Scaling by Optimizing Goodness of Fit to a Nonmetric Hypothesis

Multidimensional scaling is the problem of representing n objects: geometrically by n points, so that the interpoint distances correspond in some sense to experimental dissimilarities between objects. In just what sense distances and dissimilarities should correspond has been left rather vague in most approaches, thus leaving these approaches logically incomplete. Our fundamental hypothesis is ...