Genetic Algorithms for Finite Mixture Model Based Voxel Classification in Neuroimaging
نویسندگان
چکیده
منابع مشابه
Genetic Algorithms for Finite Mixture Model Based Tissue Classification in Brain Mri
Finite mixture models (FMMs) are an indispensable tool for unsupervised classification in brain imaging. Fitting a FMM to the data leads to a complex optimization problem. This optimization problem is difficult to solve with standard local optimization methods (e.g. by the expectation maximization (EM) algorithm) if a good initialization is not available. In this paper, we propose a new global ...
متن کاملDocument Classification Using a Finite Mixture Model
We propose a new method of classifying documents into categories. We define for each category a finite mixture model based on soft clustering of words. We treat the problem of classifying documents as that of conducting statistical hypothesis testing over finite mixture models, and employ the EM algorithm to efficiently estimate parameters in a finite mixture model. Experimental results indicat...
متن کاملExplaining Heterogeneity in Risk Preferences Using a Finite Mixture Model
This paper studies the effect of the space (distance) between lotteries' outcomes on risk-taking behavior and the shape of estimated utility and probability weighting functions. Previously investigated experimental data shows a significant space effect in the gain domain. As compared to low spaced lotteries, high spaced lotteries are associated with higher risk aversion for high probabilities o...
متن کاملFinite Mixture Models and Model-Based Clustering
Finite mixture models have a long history in statistics, having been used to model pupulation heterogeneity, generalize distributional assumptions, and lately, for providing a convenient yet formal framework for clustering and classification. This paper provides a detailed review into mixture models and model-based clustering. Recent trends in the area, as well as open problems are also discussed.
متن کاملImage Texture Classification Based on Finite Gaussian Mixture Models
A novel image texture classification method based on finite Gaussian mixture models of sub-band coefficients is proposed in this paper. In the method, an image texture is first decomposed into several sub-bands, then the marginal density distribution of coefficients in each sub-band is approximated by Gaussian mixtures. The Gaussian component parameters are estimated by an “EM+MML” algorithm wh...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Transactions on Medical Imaging
سال: 2007
ISSN: 0278-0062,1558-254X
DOI: 10.1109/tmi.2007.895453