Embedding Anatomical or Functional Knowledge in Whole-Brain Multiple Kernel Learning Models
نویسندگان
چکیده
منابع مشابه
different fractionation in whole brain irradiation for multiple brain metastases
background : this study compared the efficacy of two commonly used fractionation schedules for palliative whole brain irradiation in patients with brain metastases, and assessed the association of the radiotherapy therapy oncology group (rtog) recursive partitioning analysis for brain metastases (rpa) to survival with each schedule. methods : patients with multiple (more than three) brain metas...
متن کاملScalable Kernel Embedding of Latent Variable Models∗
Kernel embedding of distributions maps distributions to the reproducing kernel Hilbert space (RKHS) of a kernel function, such that subsequent manipulations of distributions can be achieved via RKHS distances, linear and multilinear transformations, and spectral analysis. This framework has led to simple and effective nonparametric algorithms in various machine learning problems, such as featur...
متن کاملImitation Learning via Kernel Mean Embedding
Imitation learning refers to the problem where an agent learns a policy that mimics the demonstration provided by the expert, without any information on the cost function of the environment. Classical approaches to imitation learning usually rely on a restrictive class of cost functions that best explains the expert’s demonstration, exemplified by linear functions of pre-defined features on sta...
متن کاملFunctional learning through kernel
This paper reviews the functional aspects of statistical learning theory. The main point under consideration is the nature of the hypothesis set when no prior information is available but data. Within this framework we first discuss about the hypothesis set: it is a vectorial space, it is a set of pointwise defined functions, and the evaluation functional on this set is a continuous mapping. Ba...
متن کاملSparsity in Multiple Kernel Learning
The problem of multiple kernel learning based on penalized empirical risk minimization is discussed. The complexity penalty is determined jointly by the empirical L2 norms and the reproducing kernel Hilbert space (RKHS) norms induced by the kernels with a data-driven choice of regularization parameters. The main focus is on the case when the total number of kernels is large, but only a relative...
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: Neuroinformatics
سال: 2018
ISSN: 1539-2791,1559-0089
DOI: 10.1007/s12021-017-9347-8