Above are papers related to the following paper: [Paper_ID] Paper #1106298. 	[Title] Is Consistency Harmful? [Abstract] We examine the issue of consistency from a new perspective. To avoid overfitting the training data, a considerable number of current systems have sacrificed the goal of learning hypotheses that are perfectly consistent with the training instances by setting a new goal of hypothesis simplicity (Occam's razor). Instead of using simplicity as a goal, we have developed a novel approach that addresses consistency directly. In other words, our concept learner has the explicit goal of selecting the most appropriate degree of consistency with the training data. We begin this paper by exploring concept learning with less than perfect consistency. Next, we describe a system that can adapt its degree of consistency in response to feedback about predictive accuracy on test data. Finally, we present the results of initial experiments that begin to address the question of how tightly hypotheses should fit the training data for different problems. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #35. 	[Title] 4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley. [Abstract] To implement a specific application, you should only have to change the file app.c. Section 2 describes the routines in app.c in detail. If you use additional variables for your specific problem, the easiest method of making them available to other program units is to declare them in sga.h and external.h. However, take care that you do not redeclare existing variables. Two example applications files are included in the SGA-C distribution. The file app1.c performs the simple example problem included with the Pascal version; finding the maximum of x 10 , where x is an integer interpretation of a chromosome. A slightly more complex application is include in app2.c. This application illustrates two features that have been added to SGA-C. The first of these is the ithruj2int function, which converts bits i through j in a chromosome to an integer. The second new feature is the utility pointer that is associated with each population member. The example application interprets each chromosome as a set of concatenated integers in binary form. The lengths of these integer fields is determined by the user-specified value of field size, which is read in by the function app data(). The field size must be less than the smallest of the chromosome length and the length of an unsigned integer. An integer array for storing the interpreted form of each chromosome is dynamically allocated and assigned to the chromosome's utility pointer in app malloc(). The ithruj2int routine (see utility.c) is used to translate each chromosome into its associated vector. The fitness for each chromosome is simply the sum of the squares of these integers. This example application will function for any chromosome length. SGA-C is intended to be a simple program for first-time GA experimentation. It is not intended to be definitive in terms of its efficiency or the grace of its implementation. The authors are interested in the comments, criticisms, and bug reports from SGA-C users, so that the code can be refined for easier use in subsequent versions. Please email your comments to rob@galab2.mh.ua.edu, or write to TCGA: The authors gratefully acknowledge support provided by NASA under Grant NGT-50224 and support provided by the National Science Foundation under Grant CTS-8451610. We also thank Hillol Kargupta for donating his tournament selection implementation. Booker, L. B. (1982). Intelligent behavior as an adaptation to the task environment (Doctoral dissertation, Technical Report No. 243. Ann Arbor: University of Michigan, Logic of Computers Group). Dissertations Abstracts International, 43(2), 469B. (University Microfilms No. 8214966) Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #82920. 	[Title] A Survey of Evolution Strategies [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #98698. 	[Title] Adaptive Strategy Selection for Concept Learning [Abstract] In this paper, we explore the use of genetic algorithms (GAs) to construct a system called GABIL that continually learns and refines concept classification rules from its interac - tion with the environment. The performance of this system is compared with that of two other concept learners (NEWGEM and C4.5) on a suite of target concepts. From this comparison, we identify strategies responsible for the success of these concept learners. We then implement a subset of these strategies within GABIL to produce a multistrategy concept learner. Finally, this multistrategy concept learner is further enhanced by allowing the GAs to adaptively select the appropriate strategies. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #415693. 	[Title] Using Real-Valued Genetic Algorithms to Evolve Rule Sets for Classification [Abstract] In this paper, we use a genetic algorithm to evolve a set of classification rules with real-valued attributes. We show how real-valued attribute ranges can be encoded with real-valued genes and present a new uniform method for representing don't cares in the rules. We view supervised classification as an optimization problem, and evolve rule sets that maximize the number of correct classifications of input instances. We use a variant of the Pitt approach to genetic-based machine learning system with a novel conflict resolution mechanism between competing rules within the same rule set. Experimental results demonstrate the effectiveness of our proposed approach on a benchmark wine classifier system. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #263279. 	[Title] Data Analyses Using Simulated Breeding and Inductive Learning Methods [Abstract] Marketing decision making tasks require the acquisition of efficient decision rules from noisy questionnaire data. Unlike popular learning-from-example methods, in such tasks, we must interpret the characteristics of the data without clear features of the data nor pre-determined evaluation criteria. The problem is how domain experts get simple, easy-to-understand, and accurate knowledge from noisy data. This paper describes a novel method to acquire efficient decision rules from questionnaire data using both simulated breeding and inductive learning techniques. The basic ideas of the method are that simulated breeding is used to get the effective features from the questionnaire data and that inductive learning is used to acquire simple decision rules from the data. The simulated breeding is one of the Genetic Algorithm based techniques to subjectively or interactively evaluate the qualities of offspring generated by genetic operations. The proposed method has been qualitatively and quantitatively validated by a case study on consumer product questionnaire data: the acquired rules are simpler than the results from the direct application of inductive learning; a domain expert admits that they are easy to understand; and they are at the same level on the accuracy compared with the other methods. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #509233. 	[Title] A Simpler Look at Consistency [Abstract] One of the major goals of most early concept learners was to find hypotheses that were perfectly consistent with the training data. It was believed that this goal would indirectly achieve a high degree of predictive accuracy on a set of test data. Later research has partially disproved this belief. However, the issue of consistency has not yet been resolved completely. We examine the issue of consistency from a new perspective. To avoid overfitting the training data, a considerable number of current systems have sacrificed the goal of learning hypotheses that are perfectly consistent with the training instances by setting a goal of hypothesis simplicity (Occam's razor). Instead of using simplicity as a goal, we have developed a novel approach that addresses consistency directly. In other words, our concept learner has the explicit goal of selecting the most appropriate degree of consistency with the training data. We begin this paper by exploring concept learning with less than perfect consistency. Next, we describe a system that can adapt its degree of consistency in response to feedback about predictive accuracy on test data. Finally, we present the results of initial experiments that begin to address the question of how tightly hypotheses should fit the training data for different problems. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #84020. 	[Title] Classifiers: A Theoretical and Empirical Study [Abstract] This paper describes how a competitive tree learning algorithm can be derived from first principles. The algorithm approximates the Bayesian decision theoretic solution to the learning task. Comparative experiments with the algorithm and the several mature AI and statistical families of tree learning algorithms currently in use show the derived Bayesian algorithm is consistently as good or better, although sometimes at computational cost. Using the same strategy, we can design algorithms for many other supervised and model learning tasks given just a probabilistic representation for the kind of knowledge to be learned. As an illustration, a second learning algorithm is derived for learning Bayesian networks from data. Implications to incremental learning and the use of multiple models are also discussed. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #84021. 	[Title] Using Genetic Algorithms for Supervised Concept Learning [Abstract] Genetic Algorithms (GAs) have traditionally been used for non-symbolic learning tasks. In this chapter we consider the application of a GA to a symbolic learning task, supervised concept learning from examples. A GA concept learner (GABL) is implemented that learns a concept from a set of positive and negative examples. GABL is run in a batch-incremental mode to facilitate comparison with an incremental concept learner, ID5R. Preliminary results support that, despite minimal system bias, GABL is an effective concept learner and is quite competitive with ID5R as the target concept increases in complexity. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #18582. 	[Title] Knowledge-Based Genetic Learning [Abstract] Genetic algorithms have been proven to be a powerful tool within the area of machine learning. However, there are some classes of problems where they seem to be scarcely applicable, e.g. when the solution to a given problem consists of several parts that influence each other. In that case the classic genetic operators cross-over and mutation do not work very well thus preventing a good performance. This paper describes an approach to overcome this problem by using high-level genetic operators and integrating task specific but domain independent knowledge to guide the use of these operators. The advantages of this approach are shown for learning a rule base to adapt the parameters of an image processing operator path within the SOLUTION system. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1133047. 	[Title] STRUCTURAL LEARNING OF FUZZY RULES FROM NOISED EXAMPLES [Abstract] Inductive learning algorithms try to obtain the knowledge of a system from a set of examples. One of the most difficult problems in machine learning consists in getting the structure of this knowledge. We propose an algorithm able to manage with fuzzy information and able to learn the structure of the rules that represent the system. The algorithm gives a reasonable small set of fuzzy rules that represent the original set of examples. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1688. 	[Title] Using Neural Networks and Genetic Algorithms as Heuristics for NP-Complete Problems [Abstract] Paradigms for using neural networks (NNs) and genetic algorithms (GAs) to heuristically solve boolean satisfiability (SAT) problems are presented. Since SAT is NP-Complete, any other NP-Complete problem can be transformed into an equivalent SAT problem in polynomial time, and solved via either paradigm. This technique is illustrated for hamiltonian circuit (HC) problems. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #23738. 	[Title] A THEORY OF LEARNING CLASSIFICATION RULES [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #143323. 	[Title] AN EMPIRICAL APPROACH TO SOLVING THE GENERAL UTILITY PROBLEM IN SPEEDUP LEARNING [Abstract] The utility problem in speedup learning describes a common behavior of machine learning methods: the eventual degradation of performance due to increasing amounts of learned knowledge. The shape of the learning curve (cost of using a learning method vs. number of training examples) over several domains suggests a parameterized model relating performance to the amount of learned knowledge and a mechanism to limit the amount of learned knowledge for optimal performance. Many recent approaches to avoiding the utility problem in speedup learning rely on sophisticated utility measures and significant numbers of training data to accurately estimate the utility of control knowledge. Empirical results presented here and elsewhere indicate that a simple selection strategy of retaining all control rules derived from a training problem explanation quickly defines an efficient set of control knowledge from few training problems. This simple selection strategy provides a low-cost alternative to example-intensive approaches for improving the speed of a problem solver. Experimentation illustrates the existence of a minimum (representing least cost) in the learning curve which is reached after a few training examples. Stress is placed on controlling the amount of learned knowledge as opposed to which knowledge. An attempt is also made to relate domain characteristics to the shape of the learning curve. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1106492. 	[Title] Stochastically Guided Disjunctive Version Space Learning [Abstract] This paper presents an incremental concept learning approach to identiflcation of concepts with high overall accuracy. The main idea is to address the concept overlap as a central problem when learning multiple descriptions. Many traditional inductive algorithms, as those from the disjunctive version space family considered here, face this problem. The approach focuses on combinations of confldent, possibly overlapping, concepts with an original stochastic complexity formula. The focusing is e-cient because it is organized as a simulated annealing-based beam search. The experiments show that the approach is especially suitable for developing incremental learning algorithms with the following advantages: flrst, it generates highly accurate concepts; second, it overcomes to a certain degree the sensitivity to the order of examples; and third, it handles noisy examples. Please summarise all the given information.

Paper #1106298 has the title `Is Consistency Harmful?  
` and the abstract `We examine the issue of consistency from a new perspective. To avoid overfitting the training data, a considerable number of current systems have sacrificed the goal of learning hypotheses that are perfectly consistent with the training instances by setting a new goal of hypothesis simplicity (Occam's razor). Instead of using simplicity as a goal, we have developed a novel approach that addresses consistency directly. In other words, our concept learner has the explicit goal of selecting the most appropriate degree of consistency with the training data. We begin this paper by exploring concept learning with less than perfect consistency. Next, we describe a system that can adapt its degree of consistency in response to feedback about predictive accuracy on test data. Finally, we present the results of initial experiments that begin to address the question of how tightly hypotheses should fit the training data for different problems. 
`.
Question: Which category should Paper #1106298 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #1106298 has the title `Is Consistency Harmful?  
` and the abstract `We examine the issue of consistency from a new perspective. To avoid overfitting the training data, a considerable number of current systems have sacrificed the goal of learning hypotheses that are perfectly consistent with the training instances by setting a new goal of hypothesis simplicity (Occam's razor). Instead of using simplicity as a goal, we have developed a novel approach that addresses consistency directly. In other words, our concept learner has the explicit goal of selecting the most appropriate degree of consistency with the training data. We begin this paper by exploring concept learning with less than perfect consistency. Next, we describe a system that can adapt its degree of consistency in response to feedback about predictive accuracy on test data. Finally, we present the results of initial experiments that begin to address the question of how tightly hypotheses should fit the training data for different problems. 
`.
Question: Which category should Paper #1106298 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