319,460 research outputs found
Comparative Experiments on Disambiguating Word Senses: An Illustration of the Role of Bias in Machine Learning
This paper describes an experimental comparison of seven different learning
algorithms on the problem of learning to disambiguate the meaning of a word
from context. The algorithms tested include statistical, neural-network,
decision-tree, rule-based, and case-based classification techniques. The
specific problem tested involves disambiguating six senses of the word ``line''
using the words in the current and proceeding sentence as context. The
statistical and neural-network methods perform the best on this particular
problem and we discuss a potential reason for this observed difference. We also
discuss the role of bias in machine learning and its importance in explaining
performance differences observed on specific problems.Comment: 10 page
A Statistical Perspective on Randomized Sketching for Ordinary Least-Squares
We consider statistical as well as algorithmic aspects of solving large-scale
least-squares (LS) problems using randomized sketching algorithms. For a LS
problem with input data , sketching algorithms use a sketching matrix, with . Then, rather than solving the LS problem using the
full data , sketching algorithms solve the LS problem using only the
sketched data . Prior work has typically adopted an algorithmic
perspective, in that it has made no statistical assumptions on the input
and , and instead it has been assumed that the data are fixed and
worst-case (WC). Prior results show that, when using sketching matrices such as
random projections and leverage-score sampling algorithms, with ,
the WC error is the same as solving the original problem, up to a small
constant. From a statistical perspective, we typically consider the
mean-squared error performance of randomized sketching algorithms, when data
are generated according to a statistical model , where is a noise process. We provide a rigorous
comparison of both perspectives leading to insights on how they differ. To do
this, we first develop a framework for assessing algorithmic and statistical
aspects of randomized sketching methods. We then consider the statistical
prediction efficiency (PE) and the statistical residual efficiency (RE) of the
sketched LS estimator; and we use our framework to provide upper bounds for
several types of random projection and random sampling sketching algorithms.
Among other results, we show that the RE can be upper bounded when while the PE typically requires the sample size to be substantially
larger. Lower bounds developed in subsequent results show that our upper bounds
on PE can not be improved.Comment: 27 pages, 5 figure
Statistical Comparison among Brain Networks with Popular Network Measurement Algorithms
In this research, a number of popular network measurement algorithms have
been applied to several brain networks (based on applicability of algorithms)
for finding out statistical correlation among these popular network
measurements which will help scientists to understand these popular network
measurement algorithms and their applicability to brain networks. By analysing
the results of correlations among these network measurement algorithms,
statistical comparison among selected brain networks has also been summarized.
Besides that, to understand each brain network, the visualization of each brain
network and each brain network degree distribution histogram have been
extrapolated. Six network measurement algorithms have been chosen to apply time
to time on sixteen brain networks based on applicability of these network
measurement algorithms and the results of these network measurements are put
into a correlation method to show the relationship among these six network
measurement algorithms for each brain network. At the end, the results of the
correlations have been summarized to show the statistical comparison among
these sixteen brain networks.Comment: 22 pages, 38 figures, 19 table
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