There is great interest in supporting imprecise queries over databases today. To support such queries, the system is typically required to disambiguate parts of the user-specified query against the database, using whatever resources are intrinsically available to it (the database schema, value distributions, natural language models etc). Often, systems will also have a user-interaction log available, which can supplement their model based on their own intrinsic resources. This leads to a problem of how best to combine the system's prior ranking with insight derived from the user-interaction log. Statistical inference techniques such as maximum likelihood or Bayesian updates from a subjective prior turn out not to apply in a straightforward way due to possible noise from user search behavior and to encoding biases endemic to the system's models. In this paper, we address such learning problems in interactive data retrieval, with specific focus on type classification for user-specified query terms. We develop a novel Bayesian smoothing algorithm, Bsmooth, which is simple, fast, flexible and accurate. We analytically establish some desirable properties and show, through experiments against an independent benchmark, that the addition of such a learning layer performs much better than standard methods.
DS's belief functions to combine reliable past data' and expert ( judgemental') evidence, e.g., as applied to sea level estimation subject to climate change @cite_16 , or as applied to forecasts of innovation diffusion @cite_15 , but these differ sufficiently to the IDR use case, where no data' is available and the judgemental' information falls into either a certain or an ambiguous case.
@cite_15 @cite_16
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Estimation of extreme sea levels for high return periods is of prime importance in hydrological design and flood risk assessment. Common practice consists of inferring design levels from historical observations and assuming the distribution of extreme values to be stationary. However, in recent years, there has been a growing awareness of the necessity to integrate the effects of climate change in environmental analysis. In this paper, we present a methodology based on belief functions to combine statistical judgements with expert evidence in order to predict the future centennial sea level at a particular location, taking into account climate change. Likelihood-based belief functions derived from statistical observations are combined with random intervals encoding expert assessments of the 21st century sea level rise. Monte Carlo simulations allow us to compute belief and plausibility degrees for various hypotheses about the design parameter. A method is proposed to quantify uncertainty on statistical forecasts using the formalism of belief functions. The approach is based on two steps. In the estimation step, a belief function on the parameter space is constructed from the normalized likelihood given the observed data. In the prediction step, the variable Y to be forecasted is written as a function of the parameter θ and an auxiliary random variable Z with known distribution not depending on the parameter, a model initially proposed by Dempster for statistical inference. Propagating beliefs about θ and Z through this model yields a predictive belief function on Y. The method is demonstrated on the problem of forecasting innovation diffusion using the Bass model, yielding a belief function on the number of adopters of an innovation in some future time period, based on past adoption data. Abstract Human speech perception is robust in the face of a wide variety of distortions, both experimentally applied and naturally occurring. In these conditions, state-of-the-art automatic speech recognition (ASR) technology fails. This paper describes an approach to robust ASR which acknowledges the fact that some spectro-temporal regions will be dominated by noise. For the purposes of recognition, these regions are treated as missing or unreliable. The primary advantage of this viewpoint is that it makes minimal assumptions about any noise background. Instead, reliable regions are identified, and subsequent decoding is based on this evidence. We introduce two approaches for dealing with unreliable evidence. The first – marginalisation – computes output probabilities on the basis of the reliable evidence only. The second – state-based data imputation – estimates values for the unreliable regions by conditioning on the reliable parts and the recognition hypothesis. A further source of information is the bounds on the energy of any constituent acoustic source in an additive mixture. This additional knowledge can be incorporated into the missing data framework. These approaches are applied to continuous-density hidden Markov model (HMM)-based speech recognisers and evaluated on the TIDigits corpus for several noise conditions. Two criteria which use simple noise estimates are employed as a means of identifying reliable regions. The first treats regions which are negative after spectral subtraction as unreliable. The second uses the estimated noise spectrum to derive local signal-to-noise ratios, which are then thresholded to identify reliable data points. Both marginalisation and state-based data imputation produce a substantial performance advantage over spectral subtraction alone. The use of energy bounds leads to a further increase in performance for both approaches. While marginalisation outperforms data imputation, the latter technique allows the technique to act as a preprocessor for conventional recognisers, or in speech-enhancement applications. Many methods for planning under uncertainty operate in the belief space, i.e., the set of probability distributions over states. Although the problem is computationally hard, recent advances have shown that belief-space planning is becoming practical for many medium size problems. Some of the most successful methods utilize sampling and often rely on distances between beliefs to partially guide the search process. This paper deals with the question of what is a suitable distance function for belief space planning, which despite its importance remains unanswered. This work indicates that the rarely used Wasserstein distance (also known as Earth Mover’s Distance ( ( EMD ))) is a more suitable metric than the commonly used ( L1 ) and Kullback–Leibler ( ( KL )) for belief-space planning. Simulation results on Non-Observable Markov Decision Problems, i.e., the simplest class of belief-space planning, indicate that as the problem becomes more complex, the differences on the effectiveness of different distance functions become quite prominent. In fact, in state spaces with more than 4 dimensions, by just replacing ( L1 ) or ( KL ) distance with ( EMD ), the problems become from virtually unsolvable to solvable within a reasonable time frame. Furthermore, preliminary results on Partially Observable Markov Decision Processes indicate that point-based solvers with ( EMD ) use a smaller number of samples to generate policies with similar qualities, compared to those with ( L1 ) and ( KL ). This paper also shows that ( EMD ) carries the Lipschitz continuity of the state’s cost function to Lipschitz continuity of the expected cost of the beliefs. Such a continuity property is often critical for convergence to optimal solutions.