Above are papers related to the following paper: [Paper_ID] Paper #128202. 	[Title] Learning rules with local exceptions [Abstract] We present a learning algorithm for rule-based concept representations called ripple-down rule sets. Ripple-down rule sets allow us to deal with the exceptions for each rule separately by introducing exception rules, exception rules for each exception rule etc. up to a constant depth. These local exception rules are in contrast to decision lists, in which the exception rules must be placed into a global ordering of the rules. The localization of exceptions makes it possible to represent concepts that have no decision list representation. On the other hand, decision lists with a constant number of alternations between rules for different classes can be represented by constant depth ripple-down rule sets with only a polynomial increase in size. Our algorithm is an Occam algorithm for constant depth ripple-down rule sets and, hence, a PAC learning algorithm. It is based on repeatedly applying the greedy approximation method for the weighted set cover problem to find good exception rule sets. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #128203. 	[Title] Learning Hierarchical Rule Sets [Abstract] We present an algorithm for learning sets of rules that are organized into up to k levels. Each level can contain an arbitrary number of rules "if c then l" where l is the class associated to the level and c is a concept from a given class of basic concepts. The rules of higher levels have precedence over the rules of lower levels and can be used to represent exceptions. As basic concepts we can use Boolean attributes in the infinite attribute space model, or certain concepts defined in terms of substrings. Given a sample of m examples, the algorithm runs in polynomial time and produces a consistent concept representation of size O((log m) k n k ), where n is the size of the smallest consistent representation with k levels of rules. This implies that the algorithm learns in the PAC model. The algorithm repeatedly applies the greedy heuristics for weighted set cover. The weights are obtained from approximate solutions to previous set cover problems. 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.

Paper #128202 has the title `Learning rules with local exceptions  
` and the abstract `We present a learning algorithm for rule-based concept representations called ripple-down rule sets. Ripple-down rule sets allow us to deal with the exceptions for each rule separately by introducing exception rules, exception rules for each exception rule etc. up to a constant depth. These local exception rules are in contrast to decision lists, in which the exception rules must be placed into a global ordering of the rules. The localization of exceptions makes it possible to represent concepts that have no decision list representation. On the other hand, decision lists with a constant number of alternations between rules for different classes can be represented by constant depth ripple-down rule sets with only a polynomial increase in size. Our algorithm is an Occam algorithm for constant depth ripple-down rule sets and, hence, a PAC learning algorithm. It is based on repeatedly applying the greedy approximation method for the weighted set cover problem to find good exception rule sets.
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Question: Which category should Paper #128202 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #128202 has the title `Learning rules with local exceptions  
` and the abstract `We present a learning algorithm for rule-based concept representations called ripple-down rule sets. Ripple-down rule sets allow us to deal with the exceptions for each rule separately by introducing exception rules, exception rules for each exception rule etc. up to a constant depth. These local exception rules are in contrast to decision lists, in which the exception rules must be placed into a global ordering of the rules. The localization of exceptions makes it possible to represent concepts that have no decision list representation. On the other hand, decision lists with a constant number of alternations between rules for different classes can be represented by constant depth ripple-down rule sets with only a polynomial increase in size. Our algorithm is an Occam algorithm for constant depth ripple-down rule sets and, hence, a PAC learning algorithm. It is based on repeatedly applying the greedy approximation method for the weighted set cover problem to find good exception rule sets.
`.
Question: Which category should Paper #128202 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