Above are papers related to the following paper: [Paper_ID] Paper #37541. 	[Title] Parity: The Problem that Won't Go Away [Abstract] It is well-known that certain learning methods (e.g., the perceptron learning algorithm) cannot acquire complete, parity mappings. But it is often overlooked that state-of-the-art learning methods such as C4.5 and backpropagation cannot generalise from incomplete parity mappings. The failure of such methods to generalise on parity mappings may be sometimes dismissed on the grounds that it is `impossible' to generalise over such mappings, or that parity problems are mathematical constructs having little to do with real-world learning. However, this paper argues that such a dismissal is unwarranted. It shows that parity mappings are hard to learn because they are statistically neutral and that statistical neutrality is a property which we should expect to encounter frequently in real-world contexts. It also shows that the generalization failure on parity mappings occurs even when large, minimally incomplete mappings are used for training purposes, i.e., when claims about the impossibility of generalization are particularly suspect. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #308232. 	[Title] Extending Theory Refinement to M-of-N Rules [Abstract] In recent years, machine learning research has started addressing a problem known as theory refinement. The goal of a theory refinement learner is to modify an incomplete or incorrect rule base, representing a domain theory, to make it consistent with a set of input training examples. This paper presents a major revision of the Either propositional theory refinement system. Two issues are discussed. First, we show how run time efficiency can be greatly improved by changing from a exhaustive scheme for computing repairs to an iterative greedy method. Second, we show how to extend Either to refine M-of-N rules. The resulting algorithm, Neither (New Either), is more than an order of magnitude faster and produces significantly more accurate results with theories that fit the M-of-N format. To demonstrate the advantages of Neither, we present experimental results from two real-world domains. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1105672. 	[Title] Using Neural Networks to Automatically Refine Expert System Knowledge Bases: Experiments in the NYNEX MAX Domain [Abstract] In this paper we describe our study of applying knowledge-based neural networks to the problem of diagnosing faults in local telephone loops. Currently, NYNEX uses an expert system called MAX to aid human experts in diagnosing these faults; however, having an effective learning algorithm in place of MAX would allow easy portability between different maintenance centers, and easy updating when the phone equipment changes. We find that (i) machine learning algorithms have better accuracy than MAX, (ii) neural networks perform better than decision trees, (iii) neural network ensembles perform better than standard neural networks, (iv) knowledge-based neural networks perform better than standard neural networks, and (v) an ensemble of knowledge-based neural networks performs the best. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1114388. 	[Title] There is No Free Lunch but the Starter is Cheap: Generalisation from First Principles [Abstract] According to Wolpert's no-free-lunch (NFL) theorems [1, 2], gener-alisation in the absence of domain knowledge is necessarily a zero-sum enterprise. Good generalisation performance in one situation is always offset by bad performance in another. Wolpert notes that the theorems do not demonstrate that effective generalisation is a logical impossibility but merely that a learner's bias (or assumption set) is of key importance Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #46491. 	[Title] ID2-of-3: Constructive Induction of M of-N Concepts for Discriminators in Decision Trees [Abstract] We discuss an approach to constructing composite features during the induction of decision trees. The composite features correspond to m-of-n concepts. There are three goals of this research. First, we explore a family of greedy methods for building m-of-n concepts (one of which, GS, is described in this paper). Second, we show how these concepts can be formed as internal nodes of decision trees, serving as a bias to the learner. Finally, we evaluate the method on several artificially generated and naturally occurring data sets to determine the effects of this bias. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1152290. 	[Title] Unsupervised Constructive Learning [Abstract] In constructive induction (CI), the learner's problem representation is modified as a normal part of the learning process. This is useful when the initial representation is inadequate or inappropriate. In this paper, I argue that the distinction between constructive and non-constructive methods is unclear. I propose a theoretical model which allows (a) a clean distinction to be made and (b) the process of CI to be properly motivated. I also show that although constructive induction has been used almost exclusively in the context of supervised learning, there is no reason why it cannot form a part of an unsupervised regime. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1119140. 	[Title] Truth-from-Trash Learning and the Mobot [Abstract] As natural resources become less abundant, we naturally become more interested in, and more adept at utilisation of waste materials. In doing this we are bringing to bear a ploy which is of key importance in learning | or so I argue in this paper. In the `Truth from Trash' model, learning is viewed as a process which uses environmental feedback to assemble fortuitous sensory predispositions (sensory `trash') into useful, information vehicles, i.e., `truthful' indicators of salient phenomena. The main aim will be to show how a computer implementation of the model has been used to enhance (through learning) the strategic abilities of a simulated, football playing mobot. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #137380. 	[Title] Discovering Representation Space Transformations for Learning Concept Descriptions Combining DNF and M-of-N Rules [Abstract] This paper addresses a class of learning problems that require a construction of descriptions that combine both M-of-N rules and traditional Disjunctive Normal form (DNF) rules. The presented method learns such descriptions, which we call conditional M-of-N rules, using the hypothesis-driven constructive induction approach. In this approach, the representation space is modified according to patterns discovered in the iteratively generated hypotheses. The need for the M-of-N rules is detected by observing "exclusive-or" or "equivalence" patterns in the hypotheses. These patterns indicate symmetry relations among pairs of attributes. Symmetrical attributes are combined into maximal symmetry classes. For each symmetry class, the method constructs a "counting attribute" that adds a new dimension to the representation space. The search for hypothesis in iteratively modified representation spaces is done by the standard AQ inductive rule learning algorithm. It is shown that the proposed method is capable of solving problems that would be very difficult to tackle by any of the traditional symbolic learning methods. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1120169. 	[Title] What do Constructive Learners Really Learn? [Abstract] In constructive induction (CI), the learner's problem representation is modified as a normal part of the learning process. This may be necessary if the initial representation is inadequate or inappropriate. However, the distinction between constructive and non-constructive methods appears to be highly ambiguous. Several conventional definitions of the process of constructive induction appear to include all conceivable learning processes. In this paper I argue that the process of constructive learning should be identified with that of relational learning (i.e., I suggest that Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #103482. 	[Title] Trading Spaces: Computation, Representation and the Limits of Uninformed Learning [Abstract] fl Research on this paper was partly supported by a Senior Research Leave fellowship granted by the Joint Council (SERC/MRC/ESRC) Cognitive Science Human Computer Interaction Initiative to one of the authors (Clark). Thanks to the Initiative for that support. y The order of names is arbitrary. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #85449. 	[Title] A Hypothesis-driven Constructive Induction Approach to Expanding Neural Networks [Abstract] With most machine learning methods, if the given knowledge representation space is inadequate then the learning process will fail. This is also true with methods using neural networks as the form of the representation space. To overcome this limitation, an automatic construction method for a neural network is proposed. This paper describes the BP-HCI method for a hypothesis-driven constructive induction in a neural network trained by the backpropagation algorithm. The method searches for a better representation space by analyzing the hypotheses generated in each step of an iterative learning process. The method was applied to ten problems, which include, in particular, exclusive-or, MONK2, parity-6BIT and inverse parity-6BIT problems. All problems were successfully solved with the same initial set of parameters; the extension of representation space was no more than necessary extension for each problem. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #46547. 	[Title] CONSTRUCTING CONJUNCTIVE TESTS FOR DECISION TREES [Abstract] This paper discusses an approach of constructing new attributes based on decision trees and production rules. It can improve the concepts learned in the form of decision trees by simplifying them and improving their predictive accuracy. In addition, this approach can distinguish relevant primitive attributes from irrelevant primitive attributes. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #389715. 	[Title] Constructing Nominal Xof-N Attributes [Abstract] Most constructive induction researchers focus only on new boolean attributes. This paper reports a new constructive induction algorithm, called XofN, that constructs new nominal attributes in the form of Xof-N representations. An Xof-N is a set containing one or more attribute-value pairs. For a given instance, its value corresponds to the number of its attribute-value pairs that are true. The promising preliminary experimental results, on both artificial and real-world domains, show that constructing new nominal attributes in the form of Xof-N representations can significantly improve the performance of selective induction in terms of both higher prediction accuracy and lower theory complexity. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1365. 	[Title] Cholinergic suppression of transmission may allow combined associative memory function and self-organization in the neocortex. [Abstract] Selective suppression of transmission at feedback synapses during learning is proposed as a mechanism for combining associative feedback with self-organization of feedforward synapses. Experimental data demonstrates cholinergic suppression of synaptic transmission in layer I (feedback synapses), and a lack of suppression in layer IV (feed-forward synapses). A network with this feature uses local rules to learn mappings which are not linearly separable. During learning, sensory stimuli and desired response are simultaneously presented as input. Feedforward connections form self-organized representations of input, while suppressed feedback connections learn the transpose of feedfor-ward connectivity. During recall, suppression is removed, sensory input activates the self-organized representation, and activity generates the learned response. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1118823. 	[Title] Constructing Conjunctions using Systematic Search on Decision Trees [Abstract] This paper investigates a dynamic path-based method for constructing conjunctions as new attributes for decision tree learning. It searches for conditions (attribute-value pairs) from paths to form new attributes. Compared with other hypothesis-driven new attribute construction methods, the new idea of this method is that it carries out systematic search with pruning over each path of a tree to select conditions for generating a conjunction. Therefore, conditions for constructing new attributes are dynamically decided during search. Empirically evaluation in a set of artificial and real-world domains shows that the dynamic path-based method can improve the performance of selective decision tree learning in terms of both higher prediction accuracy and lower theory complexity. In addition, it shows some performance advantages over a fixed path-based method and a fixed rule-based method for learning decision trees. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #107251. 	[Title] Continuous-valued Xof-N Attributes Versus Nominal Xof-N Attributes for Constructive Induction: A Case Study [Abstract] An Xof-N is a set containing one or more attribute-value pairs. For a given instance, its value corresponds to the number of its attribute-value pairs that are true. In this paper, we explore the characteristics and performance of continuous-valued Xof-N attributes versus nominal Xof-N attributes for constructive induction. Nominal Xof-Ns are more representationally powerful than continuous-valued Xof-Ns, but the former suffer the "fragmentation" problem, although some mechanisms such as subsetting can help to solve the problem. Two approaches to constructive induction using continuous-valued Xof-Ns are described. Continuous-valued Xof-Ns perform better than nominal ones on domains that need Xof-Ns with only one cut point. On domains that need Xof-N representations with more than one cut point, nominal Xof-Ns perform better than continuous-valued ones. Experimental results on a set of artificial and real-world domains support these statements. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #107252. 	[Title] Effects of Different Types of New Attribute on Constructive Induction [Abstract] This paper studies the effects on decision tree learning of constructing four types of attribute (conjunctive, disjunctive, Mof-N, and Xof-N representations). To reduce effects of other factors such as tree learning methods, new attribute search strategies, evaluation functions, and stopping criteria, a single tree learning algorithm is developed. With different option settings, it can construct four different types of new attribute, but all other factors are fixed. The study reveals that conjunctive and disjunctive representations have very similar performance in terms of prediction accuracy and theory complexity on a variety of concepts. Moreover, the study demonstrates that the stronger representation power of Mof-N than conjunction and disjunction and the stronger representation power of Xof-N than these three types of new attribute can be reflected in the performance of decision tree learning. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #260979. 	[Title] Separability is a Learner's Best Friend [Abstract] Geometric separability is a generalisation of linear separability, familiar to many from Minsky and Papert's analysis of the Perceptron learning method. The concept forms a novel dimension along which to conceptualise learning methods. The present paper shows how geometric separability can be defined and demonstrates that it accurately predicts the performance of a at least one empirical learning method. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1108728. 	[Title] Proceedings of the First International Workshop on Intelligent Adaptive Systems (IAS-95) Constructive Induction-based Learning Agents: [Abstract] This paper introduces a new type of intelligent agent called a constructive induction-based learning agent (CILA). This agent differs from other adaptive agents because it has the ability to not only learn how to assist a user in some task, but also to incrementally adapt its knowledge representation space to better fit the given learning task. The agents ability to autonomously make problem-oriented modifications to the originally given representation space is due to its constructive induction (CI) learning method. Selective induction (SI) learning methods, and agents based on these methods, rely on a good representation space. A good representation space has no misclassification noise, inter-correlated attributes or irrelevant attributes. Our proposed CILA has methods for overcoming all of these problems. In agent domains with poor representations, the CI-based learning agent will learn more accurate rules and be more useful than an SI-based learning agent. This paper gives an architecture for a CI-based learning agent and gives an empirical comparison of a CI and SI for a set of six abstract domains involving DNF-type (disjunctive normal form) descriptions. 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.

Paper #37541 has the title `Parity: The Problem that Won't Go Away  
` and the abstract `It is well-known that certain learning methods (e.g., the perceptron learning algorithm) cannot acquire complete, parity mappings. But it is often overlooked that state-of-the-art learning methods such as C4.5 and backpropagation cannot generalise from incomplete parity mappings. The failure of such methods to generalise on parity mappings may be sometimes dismissed on the grounds that it is `impossible' to generalise over such mappings, or that parity problems are mathematical constructs having little to do with real-world learning. However, this paper argues that such a dismissal is unwarranted. It shows that parity mappings are hard to learn because they are statistically neutral and that statistical neutrality is a property which we should expect to encounter frequently in real-world contexts. It also shows that the generalization failure on parity mappings occurs even when large, minimally incomplete mappings are used for training purposes, i.e., when claims about the impossibility of generalization are particularly suspect.
`.
Question: Which category should Paper #37541 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #37541 has the title `Parity: The Problem that Won't Go Away  
` and the abstract `It is well-known that certain learning methods (e.g., the perceptron learning algorithm) cannot acquire complete, parity mappings. But it is often overlooked that state-of-the-art learning methods such as C4.5 and backpropagation cannot generalise from incomplete parity mappings. The failure of such methods to generalise on parity mappings may be sometimes dismissed on the grounds that it is `impossible' to generalise over such mappings, or that parity problems are mathematical constructs having little to do with real-world learning. However, this paper argues that such a dismissal is unwarranted. It shows that parity mappings are hard to learn because they are statistically neutral and that statistical neutrality is a property which we should expect to encounter frequently in real-world contexts. It also shows that the generalization failure on parity mappings occurs even when large, minimally incomplete mappings are used for training purposes, i.e., when claims about the impossibility of generalization are particularly suspect.
`.
Question: Which category should Paper #37541 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