Above are papers related to the following paper: [Paper_ID] Paper #66794. 	[Title] Automated Decomposition of Model-based Learning Problems [Abstract] A new generation of sensor rich, massively distributed autonomous systems is being developed that has the potential for unprecedented performance, such as smart buildings, reconfigurable factories, adaptive traffic systems and remote earth ecosystem monitoring. To achieve high performance these massive systems will need to accurately model themselves and their environment from sensor information. Accomplishing this on a grand scale requires automating the art of large-scale modeling. This paper presents a formalization of decompositional, model-based learning (DML), a method developed by observing a modeler's expertise at decomposing large scale model estimation tasks. The method exploits a striking analogy between learning and consistency-based diagnosis. Moriarty, an implementation of DML, has been applied to thermal modeling of a smart building, demonstrating a significant improvement in learning rate. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1153031. 	[Title] Learning in the Presence of Prior Knowledge: A Case Study Using Model Calibration [Abstract] Computational models of natural systems often contain free parameters that must be set to optimize the predictive accuracy of the models. This process| called calibration|can be viewed as a form of supervised learning in the presence of prior knowledge. In this view, the fixed aspects of the model constitute the prior knowledge, and the goal is to learn values for the free parameters. We report on a series of attempts to learn parameter values for a global vegetation model called MAPSS (Mapped Atmosphere-Plant-Soil System) developed by our collaborator, Ron Neilson. Standard machine learning methods do not work with MAPSS, because the constraints introduced by the structure of the model create a very difficult non-linear optimization problem. We developed a new divide-and-conquer approach in which subsets of the parameters are calibrated while others are held constant. This approach succeeds because it is possible to select training examples that exercise only portions of the model. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #189577. 	[Title] Chain graphs for learning [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #6539. 	[Title] Quantifying Prior Determination Knowledge using the PAC Learning Model [Abstract] Prior knowledge, or bias, regarding a concept can speed up the task of learning it. Probably Approximately Correct (PAC) learning is a mathematical model of concept learning that can be used to quantify the speed up due to different forms of bias on learning. Thus far, PAC learning has mostly been used to analyze syntactic bias, such as limiting concepts to conjunctions of boolean prepositions. This paper demonstrates that PAC learning can also be used to analyze semantic bias, such as a domain theory about the concept being learned. The key idea is to view the hypothesis space in PAC learning as that consistent with all prior knowledge, syntactic and semantic. In particular, the paper presents a PAC analysis of determinations, a type of relevance knowledge. The results of the analysis reveal crisp distinctions and relations among different determinations, and illustrate the usefulness of an analysis based on the PAC model. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #159897. 	[Title] Robustness Analysis of Bayesian Networks with Finitely Generated Convex Sets of Distributions [Abstract] This paper presents exact solutions and convergent approximations for inferences in Bayesian networks associated with finitely generated convex sets of distributions. Robust Bayesian inference is the calculation of bounds on posterior values given perturbations in a probabilistic model. The paper presents exact inference algorithms and analyzes the circumstances where exact inference becomes intractable. Two classes of algorithms for numeric approximations are developed through transformations on the original model. The first transformation reduces the robust inference problem to the estimation of probabilistic parameters in a Bayesian network. The second transformation uses Lavine's bracketing algorithm to generate a sequence of maximization problems in a Bayesian network. The analysis is extended to the *-contaminated, the lower density bounded, the belief function, the sub-sigma, the density bounded, the total variation and the density ratio classes of distributions. c fl1996 Carnegie Mellon University Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #28957. 	[Title] Operations for Learning with Graphical Models decomposition techniques and the demonstration that graphical models provide [Abstract] This paper is a multidisciplinary review of empirical, statistical learning from a graphical model perspective. Well-known examples of graphical models include Bayesian networks, directed graphs representing a Markov chain, and undirected networks representing a Markov field. These graphical models are extended to model data analysis and empirical learning using the notation of plates. Graphical operations for simplifying and manipulating a problem are provided including decomposition, differentiation, and the manipulation of probability models from the exponential family. Two standard algorithm schemas for learning are reviewed in a graphical framework: Gibbs sampling and the expectation maximization algorithm. Using these operations and schemas, some popular algorithms can be synthesized from their graphical specification. This includes versions of linear regression, techniques for feed-forward networks, and learning Gaussian and discrete Bayesian networks from data. The paper concludes by sketching some implications for data analysis and summarizing how some popular algorithms fall within the framework presented. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #108962. 	[Title] BUCKET ELIMINATION: A UNIFYING FRAMEWORK FOR PROBABILISTIC INFERENCE [Abstract] Probabilistic inference algorithms for belief updating, finding the most probable explanation, the maximum a posteriori hypothesis, and the maximum expected utility are reformulated within the bucket elimination framework. This emphasizes the principles common to many of the algorithms appearing in the probabilistic inference literature and clarifies the relationship of such algorithms to nonserial dynamic programming algorithms. A general method for combining conditioning and bucket elimination is also presented. For all the algorithms, bounds on complexity are given as a function of the problem's structure. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #108963. 	[Title] Topological Parameters for time-space tradeoff [Abstract] In this paper we propose a family of algorithms combining tree-clustering with conditioning that trade space for time. Such algorithms are useful for reasoning in probabilistic and deterministic networks as well as for accomplishing optimization tasks. By analyzing the problem structure it will be possible to select from a spectrum the algorithm that best meets a given time-space specifica tion. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #28964. 	[Title] Robust Parameter Learning in Bayesian Networks with Missing Data [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #88356. 	[Title] Learning Limited Dependence Bayesian Classifiers [Abstract] We present a framework for characterizing Bayesian classification methods. This framework can be thought of as a spectrum of allowable dependence in a given probabilistic model with the Naive Bayes algorithm at the most restrictive end and the learning of full Bayesian networks at the most general extreme. While much work has been carried out along the two ends of this spectrum, there has been surprising little done along the middle. We analyze the assumptions made as one moves along this spectrum and show the tradeoffs between model accuracy and learning speed which become critical to consider in a variety of data mining domains. We then present a general induction algorithm that allows for traversal of this spectrum depending on the available computational power for carrying out induction and show its application in a number of domains with different properties. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #173863. 	[Title] Learning Belief Networks in the Presence of Missing Values and Hidden Variables [Abstract] In recent years there has been a flurry of works on learning probabilistic belief networks. Current state of the art methods have been shown to be successful for two learning scenarios: learning both network structure and parameters from complete data, and learning parameters for a fixed network from incomplete datathat is, in the presence of missing values or hidden variables. However, no method has yet been demonstrated to effectively learn network structure from incomplete data. In this paper, we propose a new method for learning network structure from incomplete data. This method is based on an extension of the Expectation-Maximization (EM) algorithm for model selection problems that performs search for the best structure inside the EM procedure. We prove the convergence of this algorithm, and adapt it for learning belief networks. We then describe how to learn networks in two scenarios: when the data contains missing values, and in the presence of hidden variables. We provide experimental results that show the effectiveness of our procedure in both scenarios. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #108974. 	[Title] Global Conditioning for Probabilistic Inference in Belief Networks [Abstract] In this paper we propose a new approach to probabilistic inference on belief networks, global conditioning, which is a simple generalization of Pearl's (1986b) method of loop-cutset conditioning. We show that global conditioning, as well as loop-cutset conditioning, can be thought of as a special case of the method of Lauritzen and Spiegelhalter (1988) as refined by Jensen et al (1990a; 1990b). Nonetheless, this approach provides new opportunities for parallel processing and, in the case of sequential processing, a tradeoff of time for memory. We also show how a hybrid method (Suermondt and others 1990) combining loop-cutset conditioning with Jensen's method can be viewed within our framework. By exploring the relationships between these methods, we develop a unifying framework in which the advantages of each approach can be combined successfully. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #116790. 	[Title] Sequential Update of Bayesian Network Structure [Abstract] There is an obvious need for improving the performance and accuracy of a Bayesian network as new data is observed. Because of errors in model construction and changes in the dynamics of the domains, we cannot afford to ignore the information in new data. While sequential update of parameters for a fixed structure can be accomplished using standard techniques, sequential update of network structure is still an open problem. In this paper, we investigate sequential update of Bayesian networks were both parameters and structure are expected to change. We introduce a new approach that allows for the flexible manipulation of the tradeoff between the quality of the learned networks and the amount of information that is maintained about past observations. We formally describe our approach including the necessary modifications to the scoring functions for learning Bayesian networks, evaluate its effectiveness through and empirical study, and extend it to the case of missing data. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1102646. 	[Title] Learning in the Presence of Prior Knowledge: A Case Study Using Model Calibration [Abstract] Computational models of natural systems often contain free parameters that must be set to optimize the predictive accuracy of the models. This process|called calibration|can be viewed as a form of supervised learning in the presence of prior knowledge. In this view, the fixed aspects of the model constitute the prior knowledge, and the goal is to learn correct values for the free parameters. We report on a series of attempts to learn parameter values for a global vegetation model called MAPSS (Mapped Atmosphere-Plant-Soil System) developed by our collaborator, Ron Neilson. Unfortunately, attempts to apply standard machine learning methods|specifically global error functions and gradient descent search|do not work with MAPSS, because the constraints introduced by the structure of the model (the prior knowledge) create a very difficult non-linear optimization problem. Successful calibration of MAPSS required taking a divide-and-conquer approach in which subsets of the parameters were calibrated while others were held constant. This approach was made possible by carefully selecting training sets that exercised only portions of the model and by designing error functions for each part that had desirable properties. The automated calibration tool that we have developed is currently being applied to calibrate MAPSS against a global climate data set. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #73146. 	[Title] LEARNING BAYESIAN NETWORKS WITH LOCAL STRUCTURE [Abstract] We examine a novel addition to the known methods for learning Bayesian networks from data that improves the quality of the learned networks. Our approach explicitly represents and learns the local structure in the conditional probability distributions (CPDs) that quantify these networks. This increases the space of possible models, enabling the representation of CPDs with a variable number of parameters. The resulting learning procedure induces models that better emulate the interactions present in the data. We describe the theoretical foundations and practical aspects of learning local structures and provide an empirical evaluation of the proposed learning procedure. This evaluation indicates that learning curves characterizing this procedure converge faster, in the number of training instances, than those of the standard procedure, which ignores the local structure of the CPDs. Our results also show that networks learned with local structures tend to be more complex (in terms of arcs), yet require fewer parameters. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #79817. 	[Title] Bayesian and Information-Theoretic Priors for Bayesian Network Parameters [Abstract] We consider Bayesian and information-theoretic approaches for determining non-informative prior distributions in a parametric model family. The information-theoretic approaches are based on the recently modified definition of stochastic complexity by Rissanen, and on the Minimum Message Length (MML) approach by Wallace. The Bayesian alternatives include the uniform prior, and the equivalent sample size priors. In order to be able to empirically compare the different approaches in practice, the methods are instantiated for a model family of practical importance, the family of Bayesian networks. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #35922. 	[Title] Mean Field Theory for Sigmoid Belief Networks [Abstract] We develop a mean field theory for sigmoid belief networks based on ideas from statistical mechanics. Our mean field theory provides a tractable approximation to the true probability distribution in these networks; it also yields a lower bound on the likelihood of evidence. We demonstrate the utility of this framework on a benchmark problem in statistical pattern recognition|the classification of handwritten digits. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #18774. 	[Title] On the Sample Complexity of Learning Bayesian Networks [Abstract] In recent years there has been an increasing interest in learning Bayesian networks from data. One of the most effective methods for learning such networks is based on the minimum description length (MDL) principle. Previous work has shown that this learning procedure is asymptotically successful: with probability one, it will converge to the target distribution, given a sufficient number of samples. However, the rate of this convergence has been hitherto unknown. In this work we examine the sample complexity of MDL based learning procedures for Bayesian networks. We show that the number of samples needed to learn an *-close approximation (in terms of entropy distance) with confidence ffi is O * ) 3 log 1 ffi log log 1 . This means that the sample complexity is a low-order polynomial in the error threshold and sub-linear in the confidence bound. We also discuss how the constants in this term depend on the complexity of the target distribution. Finally, we address questions of asymptotic minimality and propose a method for using the sample complexity results to speed up the learning process. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #18777. 	[Title] A Tutorial on Learning With Bayesian Networks [Abstract] Technical Report MSR-TR-95-06 Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1125469. 	[Title] Structured Representation of Complex Stochastic Systems [Abstract] This paper considers the problem of representing complex systems that evolve stochastically over time. Dynamic Bayesian networks provide a compact representation for stochastic processes. Unfortunately, they are often unwieldy since they cannot explicitly model the complex organizational structure of many real life systems: the fact that processes are typically composed of several interacting subprocesses, each of which can, in turn, be further decomposed. We propose a hierarchically structured representation language which extends both dynamic Bayesian networks and the object-oriented Bayesian network framework of [9], and show that our language allows us to describe such systems in a natural and modular way. Our language supports a natural representation for certain system characteristics that are hard to capture using more traditional frameworks. For example, it allows us to represent systems where some processes evolve at a different rate than others, or systems where the processes interact only intermittently. We provide a simple inference mechanism for our representation via translation to Bayesian networks, and suggest ways in which the inference algorithm can exploit the additional structure encoded in our representation. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #66782. 	[Title] Using Qualitative Models to Guide Inductive Learning [Abstract] This paper presents a method for using qualitative models to guide inductive learning. Our objectives are to induce rules which are not only accurate but also explainable with respect to the qualitative model, and to reduce learning time by exploiting domain knowledge in the learning process. Such ex-plainability is essential both for practical application of inductive technology, and for integrating the results of learning back into an existing knowledge-base. We apply this method to two process control problems, a water tank network and an ore grinding process used in the mining industry. Surprisingly, in addition to achieving explainability the classificational accuracy of the induced rules is also increased. We show how the value of the qualitative models can be quantified in terms of their equivalence to additional training examples, and finally discuss possible extensions. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1105505. 	[Title] A Divide-and-Conquer Approach to Learning from Prior Knowledge [Abstract] This paper introduces a new machine learning task|model calibration|and presents a method for solving a particularly difficult model calibration task that arose as part of a global climate change research project. The model calibration task is the problem of training the free parameters of a scientific model in order to optimize the accuracy of the model for making future predictions. It is a form of supervised learning from examples in the presence of prior knowledge. An obvious approach to solving calibration problems is to formulate them as global optimization problems in which the goal is to find values for the free parameters that minimize the error of the model on training data. Unfortunately, this global optimization approach becomes computationally infeasible when the model is highly nonlinear. This paper presents a new divide-and-conquer method that analyzes the model to identify a series of smaller optimization problems whose sequential solution solves the global calibration problem. This paper argues that methods of this kind|rather than global optimization techniques|will be required in order for agents with large amounts of prior knowledge to learn efficiently. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1133417. 	[Title] Logarithmic Time Parallel Bayesian Inference [Abstract] I present a parallel algorithm for exact probabilistic inference in Bayesian networks. For polytree networks with n variables, the worst-case time complexity is O(log n) on a CREW PRAM (concurrent-read, exclusive-write parallel random-access machine) with n processors, for any constant number of evidence variables. For arbitrary networks, the time complexity is O(r 3w log n) for n processors, or O(w log n) for r 3w n processors, where r is the maximum range of any variable, and w is the induced width (the maximum clique size), after moralizing and trian gulating the network. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1112686. 	[Title] Bayesian Finite Mixtures for Nonlinear Modeling of Educational data [Abstract] In this paper we discuss a Bayesian approach for finding latent classes in the data. In our approach we use finite mixture models to describe the underlying structure in the data, and demonstrate that the possibility to use full joint probability models raises interesting new prospects for exploratory data analysis. The concepts and methods discussed are illustrated with a case study using a data set from a recent educational study. The Bayesian classification approach described has been implemented, and presents an appealing addition to the standard toolbox for exploratory data analysis of educational data. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #103537. 	[Title] Discovering Structure in Continuous Variables Using Bayesian Networks [Abstract] We study Bayesian networks for continuous variables using nonlinear conditional density estimators. We demonstrate that useful structures can be extracted from a data set in a self-organized way and we present sampling techniques for belief update based on Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #445938. 	[Title] Compositional Modeling With DPNs [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #33013. 	[Title] Learning Conventions in Multiagent Stochastic Domains using Likelihood Estimates [Abstract] Fully cooperative multiagent systemsthose in which agents share a joint utility modelis of special interest in AI. A key problem is that of ensuring that the actions of individual agents are coordinated, especially in settings where the agents are autonomous decision makers. We investigate approaches to learning coordinated strategies in stochastic domains where an agent's actions are not directly observable by others. Much recent work in game theory has adopted a Bayesian learning perspective to the more general problem of equilibrium selection, but tends to assume that actions can be observed. We discuss the special problems that arise when actions are not observable, including effects on rates of convergence, and the effect of action failure probabilities and asymmetries. We also use likelihood estimates as a means of generalizing fictitious play learning models in our setting. Finally, we propose the use of maximum likelihood as a means of removing strategies from consideration, with the aim of convergence to a conventional equilibrium, at which point learning and deliberation can cease. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #94713. 	[Title] Update rules for parameter estimation in Bayesian networks [Abstract] This paper re-examines the problem of parameter estimation in Bayesian networks with missing values and hidden variables from the perspective of recent work in on-line learning [12]. We provide a unified framework for parameter estimation that encompasses both on-line learning, where the model is continuously adapted to new data cases as they arrive, and the more traditional batch learning, where a pre-accumulated set of samples is used in a one-time model selection process. In the batch case, our framework encompasses both the gradient projection algorithm [2, 3] and the EM algorithm [14] for Bayesian networks. The framework also leads to new on-line and batch parameter update schemes, including a parameterized version of EM. We provide both empirical and theoretical results indicating that parameterized EM allows faster convergence to the maximum likelihood parame ters than does standard EM. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #466170. 	[Title] on Qualitative Reasoning about Physical Systems  Deriving Monotonic Function Envelopes from Observations [Abstract] Much work in qualitative physics involves constructing models of physical systems using functional descriptions such as "flow monotonically increases with pressure." Semiquantitative methods improve model precision by adding numerical envelopes to these monotonic functions. Ad hoc methods are normally used to determine these envelopes. This paper describes a systematic method for computing a bounding envelope of a multivariate monotonic function given a stream of data. The derived envelope is computed by determining a simultaneous confidence band for a special neural network which is guaranteed to produce only monotonic functions. By composing these envelopes, more complex systems can be simulated using semiquantitative methods. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1105531. 	[Title] Belief Networks, Hidden Markov Models, and Markov Random Fields: a Unifying View [Abstract] The use of graphs to represent independence structure in multivariate probability models has been pursued in a relatively independent fashion across a wide variety of research disciplines since the beginning of this century. This paper provides a brief overview of the current status of such research with particular attention to recent developments which have served to unify such seemingly disparate topics as probabilistic expert systems, statistical physics, image analysis, genetics, decoding of error-correcting codes, Kalman filters, and speech recognition with Markov models. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #345340. 	[Title] Generalized Prioritized Sweeping [Abstract] Prioritized sweeping is a model-based reinforcement learning method that attempts to focus an agent's limited computational resources to achieve a good estimate of the value of environment states. To choose effectively where to spend a costly planning step, classic prioritized sweeping uses a simple heuristic to focus computation on the states that are likely to have the largest errors. In this paper, we introduce generalized prioritized sweeping, a principled method for generating such estimates in a representation-specific manner. This allows us to extend prioritized sweeping beyond an explicit, state-based representation to deal with compact representations that are necessary for dealing with large state spaces. We apply this method for generalized model approximators (such as Bayesian networks), and describe preliminary experiments that compare our approach with classical prioritized sweeping. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #510718. 	[Title] Learning Bayesian Networks from Incomplete Data [Abstract] Much of the current research in learning Bayesian Networks fails to effectively deal with missing data. Most of the methods assume that the data is complete, or make the data complete using fairly ad-hoc methods; other methods do deal with missing data but learn only the conditional probabilities, assuming that the structure is known. We present a principled approach to learn both the Bayesian network structure as well as the conditional probabilities from incomplete data. The proposed algorithm is an iterative method that uses a combination of Expectation-Maximization (EM) and Imputation techniques. Results are presented on synthetic data sets which show that the performance of the new algorithm is much better than ad-hoc methods for handling missing data. Please summarise all the given information.

Paper #66794 has the title `Automated Decomposition of Model-based Learning Problems  
` and the abstract `A new generation of sensor rich, massively distributed autonomous systems is being developed that has the potential for unprecedented performance, such as smart buildings, reconfigurable factories, adaptive traffic systems and remote earth ecosystem monitoring. To achieve high performance these massive systems will need to accurately model themselves and their environment from sensor information. Accomplishing this on a grand scale requires automating the art of large-scale modeling. This paper presents a formalization of decompositional, model-based learning (DML), a method developed by observing a modeler's expertise at decomposing large scale model estimation tasks. The method exploits a striking analogy between learning and consistency-based diagnosis. Moriarty, an implementation of DML, has been applied to thermal modeling of a smart building, demonstrating a significant improvement in learning rate. 
`.
Question: Which category should Paper #66794 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #66794 has the title `Automated Decomposition of Model-based Learning Problems  
` and the abstract `A new generation of sensor rich, massively distributed autonomous systems is being developed that has the potential for unprecedented performance, such as smart buildings, reconfigurable factories, adaptive traffic systems and remote earth ecosystem monitoring. To achieve high performance these massive systems will need to accurately model themselves and their environment from sensor information. Accomplishing this on a grand scale requires automating the art of large-scale modeling. This paper presents a formalization of decompositional, model-based learning (DML), a method developed by observing a modeler's expertise at decomposing large scale model estimation tasks. The method exploits a striking analogy between learning and consistency-based diagnosis. Moriarty, an implementation of DML, has been applied to thermal modeling of a smart building, demonstrating a significant improvement in learning rate. 
`.
Question: Which category should Paper #66794 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].

This paper should be classified into: Probabilistic_Methods