Active learning differs from "learning from examples" in that the learning algorithm assumes at least some control over what part of the input domain it receives information about. In some situations, active learning is provably more powerful than learning from examples alone, giving better generalization for a fixed number of training examples. In this article, we consider the problem of learning a binary concept in the absence of noise. We describe a formalism for active concept learning called selective sampling and show how it may be approximately implemented by a neural network. In selective sampling, a learner receives distribution information from the environment and queries an oracle on parts of the domain it considers "useful." We test our implementation, called an SGnetwork, on three domains and observe significant improvement in generalization.

We propose a robust learning algorithm and apply it to recurrent neural networks. This algorithm is based on filtering outliers from the data and then estimating parameters from the filtered data. The filtering removes outliers from both the target function and the inputs of the neural network. The filtering is soft in that some outliers are neither completely rejected nor accepted. To show the need for robust recurrent networks, we compare the predictive ability of least squares estimated recurrent networks on synthetic data and on the Puget Power Electric Demand time series. These investigations result in a class of recurrent neural networks, NARMA(p,q), which show advantages over feedforward neural networks for time series with a moving average component. Conventional least squares methods of fitting NARMA(p,q) neural network models are shown to suffer a lack of robustness towards outliers. This sensitivity to outliers is demonstrated on both the synthetic and real data sets. Filtering the Puget Power Electric Demand time series is shown to automatically remove the outliers due to holidays. Neural networks trained on filtered data are then shown to give better predictions than neural networks trained on unfiltered time series.

This paper presents an artificial neural network(ANN) approach t o electric load forecasting. The ANN is used to learn the relationship among past, current and future temperatures and loads. In order to provide the forecasted load, the ANN interpolates among the load and temperature data in a training data set. The average absolute errors of the one-hour and 24-hour ahead forecasts in our test on actual utility data are shown to be 1.40% and 2.06%, respectively. This compares with an average error of 4.22% for 24hour ahead forecasts with a currently used forecasting technique applied to the same data.

Abstract-Generalized time-frequency representations (GTFR's) which use cone-shaped kernels for nonstationary signal analysis are presented. The cone-shaped kernels are formulated for the GTFR's to produce simultaneously good resolution in time and frequency. Specifically, for a GFTR with a cone-shaped kernel, finite time support is maintained in the time dimension along with an enhanced spectrum in the frequency dimension, and the cross-terms are smoothed out. Experimental results on simulated data and real speech showed the advantages of the GTFR's with the cone-shaped kernels through comparisons to the spectrogram and the pseudo-Wigner distribution.

There is a considerable evidence that our perception of sound uses important features which is related to underlying signal modulations. This topic has been studied extensively via perceptual experiments, yet there are few, if any, well-developed signal processing methods which capitalize on or model these effects. We begin by summarizing evidence of the importance of modulation representations from psychophysical, physiological, and other sources. The concept of a two-dimensional joint acoustic and modulation frequency representation is proposed. A simple single sinusoidal amplitude modulator of a sinusoidal carrier is then used to illustrate properties of an unconstrained and ideal joint representation. Added constraints are required to remove or reduce undesired interference terms and to provide invertibility. It is then noted that the constraints would also apply to more general and complex cases of broader modulation and carriers. Applications in single-channel speaker separation and in audio coding are used to illustrate the applicability of this joint representation. Other applications in signal analysis and filtering are suggested

Abstruct-A general method for constructing nonnegativedefinite, joint time-frequency distributions (TFD's) satisfying the marginals of time Is(t)l' and frequency lS(f)12 is presented. As nonnegative-definite distributions with the correct marginals, these TFD's are members of the Cohen-Posch class. Several examples illustrating properties of these TFD's are presented for both synthetic and real signals. Patrick J. Loughlin

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In many pattern recognition applications, features are traditionally extracted from standard time-frequency representations (TFRs). This assumes that the implicit smoothing of, say, a spectrogram is appropriate for the classification task. Making such assumptions may degrade classification performance. In general, any time-frequency classification technique that uses a singular quadratic TFR (e.g., the spectrogram) as a source of features will never surpass the performance of the same technique using a regular quadratic TFR (e.g., Rihaczek or Wigner-Ville). Any TFR that is not regular is said to be singular. Use of a singular quadratic TFR implicitly discards information without explicitly determining if it is germane to the classification task. We propose smoothing regular quadratic TFRs to retain only that information that is essential for classification. We call the resulting quadratic TFRs class-dependent TFRs. This approach makes no a priori assumptions about the amount and type of time-frequency smoothing required for classification. The performance of our approach is demonstrated on simulated and real data. The simulated study indicates that the performance can approach the Bayes optimal classifier. The real-world pilot studies involved helicopter fault diagnosis and radar transmitter identification.