Above are papers related to the following paper: [Paper_ID] Paper #252715. 	[Title] EMERALD: An Integrated System of Machine Learning and Discovery Programs to Support AI Education and [Abstract] With the rapid expansion of machine learning methods and applications, there is a strong need for computer-based interactive tools that support education in this area. The EMERALD system was developed to provide hands-on experience and an interactive demonstration of several machine learning and discovery capabilities for students in AI and cognitive science, and for AI professionals. The current version of EMERALD integrates five programs that exhibit different types of machine learning and discovery: learning rules from examples, determining structural descriptions of object classes, inventing conceptual clusterings of entities, predicting sequences of objects, and discovering equations characterizing collections of quantitative and qualitative data. EMERALD extensively uses color graphic capabilities, voice synthesis, and a natural language representation of the knowledge acquired by the learning programs. Each program is presented as a "learning robot," which has its own "personality," expressed by its icon, its voice, the comments it generates during the learning process, and the results of learning presented as natural language text and/or voice output. Users learn about the capabilities of each "robot" both by being challenged to perform some learning tasks themselves, and by creating their own similar tasks to challenge the "robot." EMERALD is an extension of ILLIAN, an initial, much smaller version that toured eight major US Museums of Science, and was seen by over half a million visitors. EMERALD's architecture allows it to incorporate new programs and new capabilities. The system runs on SUN workstations, and is available to universities and educational institutions. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #20193. 	[Title] The Structure-Mapping Engine: Algorithm and Examples [Abstract] This paper describes the Structure-Mapping Engine (SME), a program for studying analogical processing. SME has been built to explore Gentner's Structure-mapping theory of analogy, and provides a "tool kit" for constructing matching algorithms consistent with this theory. Its flexibility enhances cognitive simulation studies by simplifying experimentation. Furthermore, SME is very efficient, making it a useful component in machine learning systems as well. We review the Structure-mapping theory and describe the design of the engine. We analyze the complexity of the algorithm, and demonstrate that most of the steps are polynomial, typically bounded by O (N 2 ). Next we demonstrate some examples of its operation taken from our cognitive simulation studies and work in machine learning. Finally, we compare SME to other analogy programs and discuss several areas for future work. This paper appeared in Artificial Intelligence, 41, 1989, pp 1-63. For more information, please contact forbus@ils.nwu.edu Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #3236. 	[Title] MLC A Machine Learning Library in C [Abstract] We present MLC ++ , a library of C ++ classes and tools for supervised Machine Learning. While MLC ++ provides general learning algorithms that can be used by end users, the main objective is to provide researchers and experts with a wide variety of tools that can accelerate algorithm development, increase software reliability, provide comparison tools, and display information visually. More than just a collection of existing algorithms, MLC ++ is an attempt to extract commonalities of algorithms and decompose them for a unified view that is simple, coherent, and extensible. In this paper we discuss the problems MLC ++ aims to solve, the design of MLC ++ , and the current functionality. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1154500. 	[Title]  [Abstract] Empirical Learning Results in POLLYANNA The value of empirical learning is demonstrated by results of testing the theory space search (TSS) component of POLLYANNA. Empirical data shows approximations generated from generic simplifying assumptions to have widely varying levels of accuracy and efficiency. The candidate theory space includes some theories with Pareto optimal combinations of accuracy and efficiency, as well as others that are non-optimal. Empirical learning is thus needed to separate the optimal theories from the non-optimal ones. It works as a filter on the process of generating approximations from generic simplifying assumptions. Empirical tests serve an additional purpose as well. Theory space search collects data that precisely characterizes the tradeoff between accuracy and efficiency among the candidate approximate theories. The tradeoff data can be used to select a theory that best balances the competing objectives of accuracy and efficiency in a manner appropriate to the intended performance context. The feasibility of empirical learning is also addressed by results of testing the theory space search component of POLLYANNA. In order for empirical testing to be feasible, candidate approximate theories must be operationally usable. Candidate hearts theories generated by POLLYANNA are shown to be operationally usable by experimental results from the theory space search (TSS) phase of learning. They run on a real machine producing results that can be compared with training examples. Feasibility also depends on the information and computation costs of empirical testing. Information costs result from the need to supply the system with training examples. Computation costs result from the need to execute candidate theories. Both types of costs grow with the numbers of candidate theories to be tested. Experimental results show that empirical testing in POLLYANNA is limited more by the computation costs of executing candidate theories than by the information costs of obtaining many training examples. POLLYANNA contrasts in this respect with traditional inductive learning systems. The feasibility of empirical learning depends also on the intended performance context, and on the resources available in the context of learning. Measurements from the theory space search phase indicate that TSS algorithms performing exhaustive search would not be feasible for the hearts domain, although they may be feasible for other applications. TSS algorithms that avoid exhaustive search hold considerably more promise. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #97892. 	[Title] Correcting Imperfect Domain Theories: A Knowledge-Level Analysis [Abstract] Explanation-Based Learning [Mitchell et al., 1986; DeJong and Mooney, 1986] has shown promise as a powerful analytical learning technique. However, EBL is severely hampered by the requirement of a complete and correct domain theory for successful learning to occur. Clearly, in non-trivial domains, developing such a domain theory is a nearly impossible task. Therefore, much research has been devoted to understanding how an imperfect domain theory can be corrected and extended during system performance. In this paper, we present a characterization of this problem, and use it to analyze past research in the area. Past characterizations of the problem (e.g, [Mitchell et al., 1986; Rajamoney and DeJong, 1987]) have viewed the types of performance errors caused by a faulty domain theory as primary. In contrast, we focus primarily on the types of knowledge deficiencies present in the theory, and from these derive the types of performance errors that can result. Correcting the theory can be viewed as a search through the space of possible domain theories, with a variety of knowledge sources that can be used to guide the search. We examine the types of knowledge used by a variety of past systems for this purpose. The hope is that this analysis will indicate the need for a "universal weak method" of domain theory correction, in which different sources of knowledge for theory correction can be freely and flexibly combined. 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 #212777. 	[Title] Inductive Learning of Characteristic Concept Descriptions from Small Sets of Classified Examples [Abstract] This paper presents a novel idea to the problem of learning concept descriptions from examples. Whereas most existing approaches rely on a large number of classified examples, the approach presented in the paper is aimed at being applicable when only a few examples are classified as positive (and negative) instances of a concept. The approach tries to take advantage of the information which can be induced from descriptions of unclassified objects using a conceptual clustering algorithm. The system Cola is described and results of applying Cola in two real-world domains are presented. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #10796. 	[Title] An Improved Algorithm for Incremental Induction of Decision Trees [Abstract] Technical Report 94-07 February 7, 1994 (updated April 25, 1994) This paper will appear in Proceedings of the Eleventh International Conference on Machine Learning. Abstract This paper presents an algorithm for incremental induction of decision trees that is able to handle both numeric and symbolic variables. In order to handle numeric variables, a new tree revision operator called `slewing' is introduced. Finally, a non-incremental method is given for finding a decision tree based on a direct metric of a candidate tree. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #10798. 	[Title] Learning physical descriptions from functional definitions, examples, Learning from examples: The effect of different conceptual [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #434. 	[Title] Learning Analytically and Inductively [Abstract] Learning is a fundamental component of intelligence, and a key consideration in designing cognitive architectures such as Soar [ Laird et al., 1986 ] . This chapter considers the question of what constitutes an appropriate general-purpose learning mechanism. We are interested in mechanisms that might explain and reproduce the rich variety of learning capabilities of humans, ranging from learning perceptual-motor skills such as how to ride a bicycle, to learning highly cognitive tasks such as how to play chess. Research on learning in fields such as cognitive science, artificial intelligence, neurobiology, and statistics has led to the identification of two distinct classes of learning methods: inductive and analytic. Inductive methods, such as neural network Backpropagation, learn general laws by finding statistical correlations and regularities among a large set of training examples. In contrast, analytical methods, such as Explanation-Based Learning, acquire general laws from many fewer training examples. They rely instead on prior knowledge to analyze individual training examples in detail, then use this analysis to distinguish relevant example features from the irrelevant. The question considered in this chapter is how to best combine inductive and analytical learning in an architecture that seeks to cover the range of learning exhibited by intelligent systems such as humans. We present a specific learning mechanism, Explanation Based Neural Network learning (EBNN), that blends these two types of learning, and present experimental results demonstrating its ability to learn control strategies for a mobile robot using Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #601561. 	[Title] Applying a Machine Learning Workbench: Experience with Agricultural Databases [Abstract] This paper reviews our experience with the application of machine learning techniques to agricultural databases. We have designed and implemented a machine learning workbench, WEKA, which permits rapid experimentation on a given dataset using a variety of machine learning schemes, and has several facilities for interactive investigation of the data: preprocessing attributes, evaluating and comparing the results of different schemes, and designing comparative experiments to be run offline. We discuss the partnership between agricultural scientist and machine learning researcher that our experience has shown to be vital to success. We review in some detail a particular agricultural application concerned with the culling of dairy herds. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #18619. 	[Title] Heterogeneous Uncertainty Sampling for Supervised Learning [Abstract] Uncertainty sampling methods iteratively request class labels for training instances whose classes are uncertain despite the previous labeled instances. These methods can greatly reduce the number of instances that an expert need label. One problem with this approach is that the classifier best suited for an application may be too expensive to train or use during the selection of instances. We test the use of one classifier (a highly efficient probabilistic one) to select examples for training another (the C4.5 rule induction program). Despite being chosen by this heterogeneous approach, the uncertainty samples yielded classifiers with lower error rates than random samples ten times larger. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #8703. 	[Title] Theory Refinement Combining Analytical and Empirical Methods [Abstract] This article describes a comprehensive approach to automatic theory revision. Given an imperfect theory, the approach combines explanation attempts for incorrectly classified examples in order to identify the failing portions of the theory. For each theory fault, correlated subsets of the examples are used to inductively generate a correction. Because the corrections are focused, they tend to preserve the structure of the original theory. Because the system starts with an approximate domain theory, in general fewer training examples are required to attain a given level of performance (classification accuracy) compared to a purely empirical system. The approach applies to classification systems employing a propositional Horn-clause theory. The system has been tested in a variety of application domains, and results are presented for problems in the domains of molecular biology and plant disease diagnosis. Please summarise all the given information.

Paper #252715 has the title `EMERALD: An Integrated System of Machine Learning and Discovery Programs to Support AI Education and
` and the abstract `With the rapid expansion of machine learning methods and applications, there is a strong need for computer-based interactive tools that support education in this area. The EMERALD system was developed to provide hands-on experience and an interactive demonstration of several machine learning and discovery capabilities for students in AI and cognitive science, and for AI professionals. The current version of EMERALD integrates five programs that exhibit different types of machine learning and discovery: learning rules from examples, determining structural descriptions of object classes, inventing conceptual clusterings of entities, predicting sequences of objects, and discovering equations characterizing collections of quantitative and qualitative data. EMERALD extensively uses color graphic capabilities, voice synthesis, and a natural language representation of the knowledge acquired by the learning programs. Each program is presented as a "learning robot," which has its own "personality," expressed by its icon, its voice, the comments it generates during the learning process, and the results of learning presented as natural language text and/or voice output. Users learn about the capabilities of each "robot" both by being challenged to perform some learning tasks themselves, and by creating their own similar tasks to challenge the "robot." EMERALD is an extension of ILLIAN, an initial, much smaller version that toured eight major US Museums of Science, and was seen by over half a million visitors. EMERALD's architecture allows it to incorporate new programs and new capabilities. The system runs on SUN workstations, and is available to universities and educational institutions. 
`.
Question: Which category should Paper #252715 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #252715 has the title `EMERALD: An Integrated System of Machine Learning and Discovery Programs to Support AI Education and
` and the abstract `With the rapid expansion of machine learning methods and applications, there is a strong need for computer-based interactive tools that support education in this area. The EMERALD system was developed to provide hands-on experience and an interactive demonstration of several machine learning and discovery capabilities for students in AI and cognitive science, and for AI professionals. The current version of EMERALD integrates five programs that exhibit different types of machine learning and discovery: learning rules from examples, determining structural descriptions of object classes, inventing conceptual clusterings of entities, predicting sequences of objects, and discovering equations characterizing collections of quantitative and qualitative data. EMERALD extensively uses color graphic capabilities, voice synthesis, and a natural language representation of the knowledge acquired by the learning programs. Each program is presented as a "learning robot," which has its own "personality," expressed by its icon, its voice, the comments it generates during the learning process, and the results of learning presented as natural language text and/or voice output. Users learn about the capabilities of each "robot" both by being challenged to perform some learning tasks themselves, and by creating their own similar tasks to challenge the "robot." EMERALD is an extension of ILLIAN, an initial, much smaller version that toured eight major US Museums of Science, and was seen by over half a million visitors. EMERALD's architecture allows it to incorporate new programs and new capabilities. The system runs on SUN workstations, and is available to universities and educational institutions. 
`.
Question: Which category should Paper #252715 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: Rule_Learning