6,388 research outputs found
Efficient Optimization of Performance Measures by Classifier Adaptation
In practical applications, machine learning algorithms are often needed to
learn classifiers that optimize domain specific performance measures.
Previously, the research has focused on learning the needed classifier in
isolation, yet learning nonlinear classifier for nonlinear and nonsmooth
performance measures is still hard. In this paper, rather than learning the
needed classifier by optimizing specific performance measure directly, we
circumvent this problem by proposing a novel two-step approach called as CAPO,
namely to first train nonlinear auxiliary classifiers with existing learning
methods, and then to adapt auxiliary classifiers for specific performance
measures. In the first step, auxiliary classifiers can be obtained efficiently
by taking off-the-shelf learning algorithms. For the second step, we show that
the classifier adaptation problem can be reduced to a quadratic program
problem, which is similar to linear SVMperf and can be efficiently solved. By
exploiting nonlinear auxiliary classifiers, CAPO can generate nonlinear
classifier which optimizes a large variety of performance measures including
all the performance measure based on the contingency table and AUC, whilst
keeping high computational efficiency. Empirical studies show that CAPO is
effective and of high computational efficiency, and even it is more efficient
than linear SVMperf.Comment: 30 pages, 5 figures, to appear in IEEE Transactions on Pattern
Analysis and Machine Intelligence, 201
The Structure of the Cold Neutral ISM on 10-100 Astronomical Unit Scales
We have used the Very Long Baseline Array (VLBA) and the Very Large Array
(VLA) to image Galactic neutral hydrogen in absorption towards four compact
extragalactic radio sources with 10 milliarcsecond resolution. Previous VLBA
data by Faison et al (1998) have shown the existence of prominent structures in
the direction of the extragalactic source 3C~138 with scale sizes of 10-20 AU
with changes in HI optical depth in excess of 0.8 0.1. In this paper we
confirm the small scale \hi optical depth variations toward 3C~147 suggested
earlier at a level up to 20 % 5% . The sources 3C~119, 2352+495 and
0831+557 show no significant change in \hi optical depth across the sources
with one sigma limits of 30%, 50%, and 100%. Of the seven sources recently
investigated with the VLBA and VLA, only 3C~138 and 3C~147 show statistically
significant variations in HI opacities.
Deshpande (2000) have attempted to explain the observed small-scale structure
as an extension of the observed power spectrum of structure on parsec size
scales. The predictions of Deshpande (2000) are consistent with the VLBA HI
data observed in the directions of a number of sources, including 3C~147, but
are not consistent with our previous observations of the HI opacity structure
toward 3C~138
On Markov parameters in system identification
A detailed discussion of Markov parameters in system identification is given. Different forms of input-output representation of linear discrete-time systems are reviewed and discussed. Interpretation of sampled response data as Markov parameters is presented. Relations between the state-space model and particular linear difference models via the Markov parameters are formulated. A generalization of Markov parameters to observer and Kalman filter Markov parameters for system identification is explained. These extended Markov parameters play an important role in providing not only a state-space realization, but also an observer/Kalman filter for the system of interest
Identification of linear multivariable systems from a single set of data by identification of observers with assigned real eigenvalues
A formulation is presented for identification of linear multivariable from a single set of input-output data. The identification method is formulated with the mathematical framework of learning identifications, by extension of the repetition domain concept to include shifting time intervals. This method contrasts with existing learning approaches that require data from multiple experiments. In this method, the system input-output relationship is expressed in terms of an observer, which is made asymptotically stable by an embedded real eigenvalue assignment procedure. Through this relationship, the Markov parameters of the observer are identified. The Markov parameters of the actual system are recovered from those of the observer, and then used to obtain a state space model of the system by standard realization techniques. The basic mathematical formulation is derived, and numerical examples presented to illustrate
Effect of Local Population Uncertainty on Cooperation in Bacteria
Bacteria populations rely on mechanisms such as quorum sensing to coordinate
complex tasks that cannot be achieved by a single bacterium. Quorum sensing is
used to measure the local bacteria population density, and it controls
cooperation by ensuring that a bacterium only commits the resources for
cooperation when it expects its neighbors to reciprocate. This paper proposes a
simple model for sharing a resource in a bacterial environment, where knowledge
of the population influences each bacterium's behavior. Game theory is used to
model the behavioral dynamics, where the net payoff (i.e., utility) for each
bacterium is a function of its current behavior and that of the other bacteria.
The game is first evaluated with perfect knowledge of the population. Then, the
unreliability of diffusion introduces uncertainty in the local population
estimate and changes the perceived payoffs. The results demonstrate the
sensitivity to the system parameters and how population uncertainty can
overcome a lack of explicit coordination.Comment: 5 pages, 6 figures. Will be presented as an invited paper at the 2017
IEEE Information Theory Workshop in November 2017 in Kaohsiung, Taiwa
Comparison of several system identification methods for flexible structures
In the last few years various methods of identifying structural dynamics models from modal testing data have appeared. A comparison is presented of four of these algorithms: the Eigensystem Realization Algorithm (ERA), the modified version ERA/DC where DC indicated that it makes use of data correlation, the Q-Markov Cover algorithm, and an algorithm due to Moonen, DeMoor, Vandenberghe, and Vandewalle. The comparison is made using a five mode computer module of the 20 meter Mini-Mast truss structure at NASA Langley Research Center, and various noise levels are superimposed to produced simulated data. The results show that for the example considered ERA/DC generally gives the best results; that ERA/DC is always at least as good as ERA which is shown to be a special case of ERA/DC; that Q-Markov requires the use of significantly more data than ERA/DC to produce comparable results; and that is some situations Q-Markov cannot produce comparable results
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