Above are papers related to the following paper: [Paper_ID] Paper #10174. 	[Title] Knowing What Doesn't Matter: Exploiting Omitted Superfluous Data [Abstract] Most inductive inference algorithms (i.e., "learners") work most effectively when their training data contain completely specified labeled samples. In many diagnostic tasks, however, the data will include the values of only some of the attributes; we model this as a blocking process that hides the values of those attributes from the learner. While blockers that remove the values of critical attributes can handicap a learner, this paper instead focuses on blockers that remove only superfluous attribute values, i.e., values that are not needed to classify an instance, given the values of the other unblocked attributes. We first motivate and formalize this model of "superfluous-value blocking," and then demonstrate that these omissions can be useful, by showing that certain classes that seem hard to learn in the general PAC model | viz., decision trees | are trivial to learn in this setting, and can even be learned in a manner that is very robust to classification noise. We also discuss how this model can be extended to deal with (1) theory revision (i.e., modifying an existing decision tree); (2) "complex" attributes (which correspond to combinations of other atomic attributes); (3) blockers that occasionally include superfluous values or exclude re quired values; and (4) other hypothesis classes (e.g., DNF formulae). Declaration: This paper has not already been accepted by and is not currently under review for a journal or another conference, nor will it be submitted for such during IJCAI's review period. fl This is an extended version of a paper that appeared in working notes of the 1994 AAAI Fall Symposium on "Relevance", New Orleans, November 1994. y Authors listed alphabetically. We gratefully acknowledge receiving helpful comments from Dale Schuurmans and George Drastal. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #10183. 	[Title] Concept Learning and Heuristic Classification in Weak-Theory Domains 1 [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #137790. 	[Title] ON THE SAMPLE COMPLEXITY OF FINDING GOOD SEARCH STRATEGIES 2n trials of each undetermined experiment [Abstract] A satisficing search problem consists of a set of probabilistic experiments to be performed in some order, without repetitions, until a satisfying configuration of successes and failures has been reached. The cost of performing the experiments depends on the order chosen. Earlier work has concentrated on finding optimal search strategies in special cases of this model, such as search trees and and-or graphs, when the cost function and the success probabilities for the experiments are given. In contrast, we study the complexity of "learning" an approximately optimal search strategy when some of the success probabilities are not known at the outset. Working in the fully general model, we show that if n is the number of unknown probabilities, and C is the maximum cost of performing all the experiments, then Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #10186. 	[Title] Learning Default Concepts [Abstract] Classical concepts, based on necessary and sufficient defining conditions, cannot classify logically insufficient object descriptions. Many reasoning systems avoid this limitation by using "default concepts" to classify incompletely described objects. We address the task of learning such default concepts from observational data. We model the underlying performance task | classifying incomplete examples | by a probabilistic process where random test examples are passed through a "blocker" that can hide object attributes from the classifier. We then address the task of learning accurate default concepts from random training examples. We survey the learning techniques that have been proposed for this task in the machine learning and knowledge representation literatures, investigate the relative merits of each, and show that a superior learning technique can be developed from well known statistical principles. Finally, we extend Valiant's pac-learning framework to this context and obtain a number of useful learnability results. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #2354. 	[Title] A Statistical Approach to Solving the EBL Utility Problem [Abstract] Many "learning from experience" systems use information extracted from problem solving experiences to modify a performance element PE, forming a new element PE 0 that can solve these and similar problems more efficiently. However, as transformations that improve performance on one set of problems can degrade performance on other sets, the new PE 0 is not always better than the original PE; this depends on the distribution of problems. We therefore seek the performance element whose expected performance, over this distribution, is optimal. Unfortunately, the actual distribution, which is needed to determine which element is optimal, is usually not known. Moreover, the task of finding the optimal element, even knowing the distribution, is intractable for most interesting spaces of elements. This paper presents a method, palo, that side-steps these problems by using a set of samples to estimate the unknown distribution, and by using a set of transformations to hill-climb to a local optimum. This process is based on a mathematically rigorous form of utility analysis: in particular, it uses statistical techniques to determine whether the result of a proposed transformation will be better than the original system. We also present an efficient way of implementing this learning system in the context of a general class of performance elements, and include empirical evidence that this approach can work effectively. fl Much of this work was performed at the University of Toronto, where it was supported by the Institute for Robotics and Intelligent Systems and by an operating grant from the National Science and Engineering Research Council of Canada. We also gratefully acknowledge receiving many helpful comments from William Cohen, Dave Mitchell, Dale Schuurmans and the anonymous referees. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #86840. 	[Title] Learning Active Classifiers [Abstract] Many classification algorithms are "passive", in that they assign a class-label to each instance based only on the description given, even if that description is incomplete. In contrast, an active classifier can | at some cost | obtain the values of missing attributes, before deciding upon a class label. The expected utility of using an active classifier depends on both the cost required to obtain the additional attribute values and the penalty incurred if it outputs the wrong classification. This paper considers the problem of learning near-optimal active classifiers, using a variant of the probably-approximately-correct (PAC) model. After defining the framework | which is perhaps the main contribution of this paper | we describe a situation where this task can be achieved efficiently, but then show that the task is often intractable. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #74749. 	[Title] Probably Approximately Optimal Derivation Strategies [Abstract] An inference graph can have many "derivation strategies", each a particular ordering of the steps involved in reducing a given query to a sequence of database retrievals. An "optimal strategy" for a given distribution of queries is a complete strategy whose "expected cost" is minimal, where the expected cost depends on the conditional probabilities that each requested retrieval succeeds, given that a member of this class of queries is posed. This paper describes the PAO algorithm that first uses a set of training examples to approximate these probability values, and then uses these estimates to produce a "probably approximately optimal" strategy | i.e., given any *; ffi &gt; 0, PAO produces a strategy whose cost is within * of the cost of the optimal strategy, with probability greater than 1 ffi. This paper also shows how to obtain these strategies in time polynomial in 1=*, 1=ffi and the size of the inference graph, for many important classes of graphs, including all and-or trees. Please summarise all the given information.

Paper #10174 has the title `Knowing What Doesn't Matter: Exploiting Omitted Superfluous Data  
` and the abstract `Most inductive inference algorithms (i.e., "learners") work most effectively when their training data contain completely specified labeled samples. In many diagnostic tasks, however, the data will include the values of only some of the attributes; we model this as a blocking process that hides the values of those attributes from the learner. While blockers that remove the values of critical attributes can handicap a learner, this paper instead focuses on blockers that remove only superfluous attribute values, i.e., values that are not needed to classify an instance, given the values of the other unblocked attributes. We first motivate and formalize this model of "superfluous-value blocking," and then demonstrate that these omissions can be useful, by showing that certain classes that seem hard to learn in the general PAC model | viz., decision trees | are trivial to learn in this setting, and can even be learned in a manner that is very robust to classification noise. We also discuss how this model can be extended to deal with (1) theory revision (i.e., modifying an existing decision tree); (2) "complex" attributes (which correspond to combinations of other atomic attributes); (3) blockers that occasionally include superfluous values or exclude re quired values; and (4) other hypothesis classes (e.g., DNF formulae). Declaration: This paper has not already been accepted by and is not currently under review for a journal or another conference, nor will it be submitted for such during IJCAI's review period. fl This is an extended version of a paper that appeared in working notes of the 1994 AAAI Fall Symposium on "Relevance", New Orleans, November 1994. y Authors listed alphabetically. We gratefully acknowledge receiving helpful comments from Dale Schuurmans and George Drastal. 
`.
Question: Which category should Paper #10174 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #10174 has the title `Knowing What Doesn't Matter: Exploiting Omitted Superfluous Data  
` and the abstract `Most inductive inference algorithms (i.e., "learners") work most effectively when their training data contain completely specified labeled samples. In many diagnostic tasks, however, the data will include the values of only some of the attributes; we model this as a blocking process that hides the values of those attributes from the learner. While blockers that remove the values of critical attributes can handicap a learner, this paper instead focuses on blockers that remove only superfluous attribute values, i.e., values that are not needed to classify an instance, given the values of the other unblocked attributes. We first motivate and formalize this model of "superfluous-value blocking," and then demonstrate that these omissions can be useful, by showing that certain classes that seem hard to learn in the general PAC model | viz., decision trees | are trivial to learn in this setting, and can even be learned in a manner that is very robust to classification noise. We also discuss how this model can be extended to deal with (1) theory revision (i.e., modifying an existing decision tree); (2) "complex" attributes (which correspond to combinations of other atomic attributes); (3) blockers that occasionally include superfluous values or exclude re quired values; and (4) other hypothesis classes (e.g., DNF formulae). Declaration: This paper has not already been accepted by and is not currently under review for a journal or another conference, nor will it be submitted for such during IJCAI's review period. fl This is an extended version of a paper that appeared in working notes of the 1994 AAAI Fall Symposium on "Relevance", New Orleans, November 1994. y Authors listed alphabetically. We gratefully acknowledge receiving helpful comments from Dale Schuurmans and George Drastal. 
`.
Question: Which category should Paper #10174 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: Theory