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 #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 #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 #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 #3218. 	[Title] Rule Induction with CN2: Some Recent Improvements [Abstract] The CN2 algorithm induces an ordered list of classification rules from examples using entropy as its search heuristic. In this short paper, we describe two improvements to this algorithm. Firstly, we present the use of the Laplacian error estimate as an alternative evaluation function and secondly, we show how unordered as well as ordered rules can be generated. We experimentally demonstrate significantly improved performances resulting from these changes, thus enhancing the usefulness of CN2 as an inductive tool. Comparisons with Quinlan's C4.5 are also made. 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 #51834. 	[Title] Rerepresenting and Restructuring Domain Theories: A Constructive Induction Approach [Abstract] Theory revision integrates inductive learning and background knowledge by combining training examples with a coarse domain theory to produce a more accurate theory. There are two challenges that theory revision and other theory-guided systems face. First, a representation language appropriate for the initial theory may be inappropriate for an improved theory. While the original representation may concisely express the initial theory, a more accurate theory forced to use that same representation may be bulky, cumbersome, and difficult to reach. Second, a theory structure suitable for a coarse domain theory may be insufficient for a fine-tuned theory. Systems that produce only small, local changes to a theory have limited value for accomplishing complex structural alterations that may be required. Consequently, advanced theory-guided learning systems require flexible representation and flexible structure. An analysis of various theory revision systems and theory-guided learning systems reveals specific strengths and weaknesses in terms of these two desired properties. Designed to capture the underlying qualities of each system, a new system uses theory-guided constructive induction. Experiments in three domains show improvement over previous theory-guided systems. This leads to a study of the behavior, limitations, and potential of theory-guided constructive induction. 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 #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 #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 #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.

Paper #1105505 has the title `A Divide-and-Conquer Approach to Learning from Prior Knowledge  
` and the 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. 
`.
Question: Which category should Paper #1105505 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #1105505 has the title `A Divide-and-Conquer Approach to Learning from Prior Knowledge  
` and the 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. 
`.
Question: Which category should Paper #1105505 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: Neural_Networks