68 research outputs found
The Early Restart Algorithm
Consider an algorithm whose time to convergence is unknown (because of some random element in the algorithm, such as a random initial weight choice for neural network training). Consider the following strategy. Run the algorithm for a specific time T. If it has not converged by time T, cut the run short and rerun it from the start (repeat the same strategy for every run). This so-called restart mechanism has been proposed by Fahlman (1988) in the context of backpropagation training. It is advantageous in problems that are prone to local minima or when there is a large variability in convergence time from run to run, and may lead to a speed-up in such cases. In this article, we analyze theoretically the restart mechanism, and obtain conditions on the probability density of the convergence time for which restart will improve the expected convergence time. We also derive the optimal restart time. We apply the derived formulas to several cases, including steepest-descent algorithms
Bankruptcy prediction for credit risk using neural networks: A survey and new results
The prediction of corporate bankruptcies is an important and widely studied topic since it can have significant impact on bank lending decisions and profitability. This work presents two contributions. First we review the topic of bankruptcy prediction, with emphasis on neural-network (NN) models. Second, we develop an NN bankruptcy prediction model. Inspired by one of the traditional credit risk models developed by Merton (1974), we propose novel indicators for the NN system. We show that the use of these indicators in addition to traditional financial ratio indicators provides a significant improvement in the (out-of-sample) prediction accuracy (from 81.46% to 85.5% for a three-year-ahead forecast)
Learning on a General Network
This paper generalizes the back-propagation method to a general network containing feedback
connections. The network model considered consists of interconnected groups of neurons,
where each group could be fully interconnected (it could have feedback connections, with possibly
asymmetric weights), but no loops between the groups are allowed. A stochastic descent
algorithm is applied, under a certain inequality constraint on each intra-group weight matrix
which ensures for the network to possess a unique equilibrium state for every input
A New Monte Carlo Based Algorithm for the Gaussian Process Classification Problem
Gaussian process is a very promising novel technology that has been applied
to both the regression problem and the classification problem. While for the
regression problem it yields simple exact solutions, this is not the case for
the classification problem, because we encounter intractable integrals. In this
paper we develop a new derivation that transforms the problem into that of
evaluating the ratio of multivariate Gaussian orthant integrals. Moreover, we
develop a new Monte Carlo procedure that evaluates these integrals. It is based
on some aspects of bootstrap sampling and acceptancerejection. The proposed
approach has beneficial properties compared to the existing Markov Chain Monte
Carlo approach, such as simplicity, reliability, and speed
Optimal Neural Spike Classification
Being able to record the electrical activities of a number of neurons simultaneously is likely
to be important in the study of the functional organization of networks of real neurons. Using
one extracellular microelectrode to record from several neurons is one approach to studying
the response properties of sets of adjacent and therefore likely related neurons. However, to
do this, it is necessary to correctly classify the signals generated by these different neurons.
This paper considers this problem of classifying the signals in such an extracellular recording,
based upon their shapes, and specifically considers the classification of signals in the case when
spikes overlap temporally
ARCOQ: Arabic Closest Opposite Questions Dataset
This paper presents a dataset for closest opposite questions in Arabic
language. The dataset is the first of its kind for the Arabic language. It is
beneficial for the assessment of systems on the aspect of antonymy detection.
The structure is similar to that of the Graduate Record Examination (GRE)
closest opposite questions dataset for the English language. The introduced
dataset consists of 500 questions, each contains a query word for which the
closest opposite needs to be determined from among a set of candidate words.
Each question is also associated with the correct answer. We publish the
dataset publicly in addition to providing standard splits of the dataset into
development and test sets. Moreover, the paper provides a benchmark for the
performance of different Arabic word embedding models on the introduced
dataset
Introduction to the special issue on neural networks in financial engineering
There are several phases that an emerging field goes through before it reaches maturity, and computational finance is no exception. There is usually a trigger for the birth of the field. In our case, new techniques such as neural networks, significant progress in computing technology, and the need for results that rely on more realistic assumptions inspired new researchers to revisit the traditional problems of finance, problems that have often been tackled by introducing simplifying assumptions in the past. The result has been a wealth of new approaches to these time-honored problems, with significant improvements in many cases
Adaptive Filtering Using Recurrent Neural Networks
A method for adaptive (or, optionally, nonadaptive) filtering has been developed for estimating the states of complex process systems (e.g., chemical plants, factories, or manufacturing processes at some level of abstraction) from time series of measurements of system inputs and outputs. The method is based partly on the fundamental principles of the Kalman filter and partly on the use of recurrent neural networks. The standard Kalman filter involves an assumption of linearity of the mathematical model used to describe a process system. The extended Kalman filter accommodates a nonlinear process model but still requires linearization about the state estimate. Both the standard and extended Kalman filters involve the often unrealistic assumption that process and measurement noise are zero-mean, Gaussian, and white. In contrast, the present method does not involve any assumptions of linearity of process models or of the nature of process noise; on the contrary, few (if any) assumptions are made about process models, noise models, or the parameters of such models. In this regard, the method can be characterized as one of nonlinear, nonparametric filtering. The method exploits the unique ability of neural networks to approximate nonlinear functions. In a given case, the process model is limited mainly by limitations of the approximation ability of the neural networks chosen for that case. Moreover, despite the lack of assumptions regarding process noise, the method yields minimum- variance filters. In that they do not require statistical models of noise, the neural- network-based state filters of this method are comparable to conventional nonlinear least-squares estimators
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