Input Space Coverage Matters
نویسندگان
چکیده
منابع مشابه
Input Space
We have studied the application of diierent classiication algorithms in the analysis of simulated high energy physics data. Whereas Neural Network algorithms have become a standard tool for data analysis, the performance of other classiiers such as Support Vector Machines has not yet been tested in this environment. We chose two diierent problems to compare the performance of a Support Vector M...
متن کاملSize Matters: Logarithmic Space Is Real Time
We show that all the problems solvable by a nondeterministic machine with logarithmic work space (NL) can be solved in real time by a parallel machine, no matter how tight the real-time constraints are. We also show that several other real-time problems are in effect solvable in nondeterministic logarithmic space once their real-time constraints are dropped and they become non-real-time. We thu...
متن کاملMeasurement matters – Input price proxies and bank efficiency in Germany
Most bank efficiency studies that use stochastic frontier analysis (SFA) employ each bank’s own implicit input price when estimating efficient frontiers. But the theoretical foundation of most studies is a cost minimisation and/ or profit maximisation problem assuming perfect input markets. At the very least, traditional input price proxies therefore contain substantial measurement error. In th...
متن کاملInput space versus feature space in kernel-based methods
This paper collects some ideas targeted at advancing our understanding of the feature spaces associated with support vector (SV) kernel functions. We first discuss the geometry of feature space. In particular, we review what is known about the shape of the image of input space under the feature space map, and how this influences the capacity of SV methods. Following this, we describe how the me...
متن کاملCoverage of space in Boolean models
Abstract: For a marked point process {(xi, Si)i≥1} with {xi ∈ Λ : i ≥ 1} being a point process on Λ ⊆ R and {Si ⊆ R d : i ≥ 1} being random sets consider the region C = ∪i≥1(xi + Si). This is the covered region obtained from the Boolean model {(xi + Si) : i ≥ 1}. The Boolean model is said to be completely covered if Λ ⊆ C almost surely. If Λ is an infinite set such that s + Λ ⊆ Λ for all s ∈ Λ ...
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: Computer
سال: 2020
ISSN: 0018-9162,1558-0814
DOI: 10.1109/mc.2019.2951980