4,717 research outputs found
Statistical Mechanics of Time Domain Ensemble Learning
Conventional ensemble learning combines students in the space domain. On the
other hand, in this paper we combine students in the time domain and call it
time domain ensemble learning. In this paper, we analyze the generalization
performance of time domain ensemble learning in the framework of online
learning using a statistical mechanical method. We treat a model in which both
the teacher and the student are linear perceptrons with noises. Time domain
ensemble learning is twice as effective as conventional space domain ensemble
learning.Comment: 10 pages, 10 figure
Statistical Mechanics of Linear and Nonlinear Time-Domain Ensemble Learning
Conventional ensemble learning combines students in the space domain. In this
paper, however, we combine students in the time domain and call it time-domain
ensemble learning. We analyze, compare, and discuss the generalization
performances regarding time-domain ensemble learning of both a linear model and
a nonlinear model. Analyzing in the framework of online learning using a
statistical mechanical method, we show the qualitatively different behaviors
between the two models. In a linear model, the dynamical behaviors of the
generalization error are monotonic. We analytically show that time-domain
ensemble learning is twice as effective as conventional ensemble learning.
Furthermore, the generalization error of a nonlinear model features
nonmonotonic dynamical behaviors when the learning rate is small. We
numerically show that the generalization performance can be improved remarkably
by using this phenomenon and the divergence of students in the time domain.Comment: 11 pages, 7 figure
A Measurement of Proper Motions of SiO Maser Sources in the Galactic Center with the VLBA
We report on the high-precision astrometric observations of maser sources
around the Galactic Center in the SiO J=1--0 v=1 and 2 lines with the VLBA
during 2001 -- 2004. With phase-referencing interferometry referred to the
radio continuum source Sgr A*, accurate positions of masers were obtained for
three detected objects: IRS 10 EE (7 epochs), IRS 15NE (2 epochs), and SiO 6
(only 1 epoch). Because circumstellar masers of these objects were resolved
into several components, proper motions for the maser sources were derived with
several different methods. Combining our VLBA results with those of the
previous VLA observations, we obtained the IRS 10EE proper motion of 76+-3 km
s^{-1} (at 8 kpc) to the south relative to Sgr A*. Almost null proper motion of
this star in the east--west direction results in a net transverse motion of the
infrared reference frame of about 30+-9 km s^{-1} to the west relative to Sgr
A*. The proper-motion data also suggests that IRS 10EE is an astrometric binary
with an unseen massive companion.Comment: High-res. figures are available at
ftp://ftp.nro.nao.ac.jp/nroreport/no656.pdf.gz . PASJ 60, No. 1 (2008) in
pres
An extracellular serine protease produced by Vibrio vulnificus NCIMB 2137, a metalloprotease-gene negative strain isolated from a diseased eel
Vibrio vulnificus is a ubiquitous estuarine microorganism but causes fatal systemic infections in immunocompromised humans, cultured eels or shrimps. An extracellular metalloprotease VVP/VvpE has been reported to be a potential virulence factor of the bacterium; however, a few strains isolated from a diseased eel or shrimp were recently found to produce a serine protease termed VvsA, but not VVP/VvpE. In the present study, we found that these strains had lost the 80 kb genomic region including the gene encoding VVP/VvpE. We also purified VvsA from the culture supernatant through ammonium sulfate fractionation, gel filtration and ion-exchange column chromatography, and the enzyme was demonstrated to be a chymotrypsin-like protease, as well as those from some vibrios. The gene vvsA was shown to constitute an operon with a downstream gene vvsB, and several Vibrio species were found to have orthologues of vvsAB. These findings indicate that the genes vvp/vvpE and vvsAB might be mobile genetic elements
Adhesion, friction, and wear of plasma-deposited thin silicon nitride films at temperatures to 700 C
The adhesion, friction, and wear behavior of silicon nitride films deposited by low- and high-frequency plasmas (30 kHz and 13.56 MHz) at various temperatures to 700 C in vacuum were examined. The results of the investigation indicated that the Si/N ratios were much greater for the films deposited at 13.56 MHz than for those deposited at 30 kHz. Amorphous silicon was present in both low- and high-frequency plasma-deposited silicon nitride films. However, more amorphous silicon occurred in the films deposited at 13.56 MHz than in those deposited at 30 kHz. Temperature significantly influenced adhesion, friction, and wear of the silicon nitride films. Wear occurred in the contact area at high temperature. The wear correlated with the increase in adhesion and friction for the low- and high-frequency plasma-deposited films above 600 and 500 C, respectively. The low- and high-frequency plasma-deposited thin silicon nitride films exhibited a capability for lubrication (low adhesion and friction) in vacuum at temperatures to 500 and 400 C, respectively
Ensemble learning of linear perceptron; Online learning theory
Within the framework of on-line learning, we study the generalization error
of an ensemble learning machine learning from a linear teacher perceptron. The
generalization error achieved by an ensemble of linear perceptrons having
homogeneous or inhomogeneous initial weight vectors is precisely calculated at
the thermodynamic limit of a large number of input elements and shows rich
behavior. Our main findings are as follows. For learning with homogeneous
initial weight vectors, the generalization error using an infinite number of
linear student perceptrons is equal to only half that of a single linear
perceptron, and converges with that of the infinite case with O(1/K) for a
finite number of K linear perceptrons. For learning with inhomogeneous initial
weight vectors, it is advantageous to use an approach of weighted averaging
over the output of the linear perceptrons, and we show the conditions under
which the optimal weights are constant during the learning process. The optimal
weights depend on only correlation of the initial weight vectors.Comment: 14 pages, 3 figures, submitted to Physical Review
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