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 #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 #262178. 	[Title] Evolution of the Topology and the Weights of Neural Networks using Genetic Programming with a [Abstract] Genetic programming is a methodology for program development, consisting of a special form of genetic algorithm capable of handling parse trees representing programs, that has been successfully applied to a variety of problems. In this paper a new approach to the construction of neural networks based on genetic programming is presented. A linear chromosome is combined to a graph representation of the network and new operators are introduced, which allow the evolution of the architecture and the weights simultaneously without the need of local weight optimization. This paper describes the approach, the operators and reports results of the application of the model to several binary classification problems. 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 #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 #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 #1114442. 	[Title] Soft Computing: the Convergence of Emerging Reasoning Technologies [Abstract] The term Soft Computing (SC) represents the combination of emerging problem-solving technologies such as Fuzzy Logic (FL), Probabilistic Reasoning (PR), Neural Networks (NNs), and Genetic Algorithms (GAs). Each of these technologies provide us with complementary reasoning and searching methods to solve complex, real-world problems. After a brief description of each of these technologies, we will analyze some of their most useful combinations, such as the use of FL to control GAs and NNs parameters; the application of GAs to evolve NNs (topologies or weights) or to tune FL controllers; and the implementation of FL controllers as NNs tuned by backpropagation-type algorithms. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #85452. 	[Title] Constructive Induction Using a Non-Greedy Strategy for Feature Selection [Abstract] We present a method for feature construction and selection that finds a minimal set of conjunctive features that are appropriate to perform the classification task. For problems where this bias is appropriate, the method outperforms other constructive induction algorithms and is able to achieve higher classification accuracy. The application of the method in the search for minimal multi-level boolean expressions is presented and analyzed with the help of some examples. 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 #3218. 	[Title] Rule Induction with CN2: Some Recent Improvements [Abstract] The CN2 algorithm induces an ordered list of classification rules from examples using entropy as its search heuristic. In this short paper, we describe two improvements to this algorithm. Firstly, we present the use of the Laplacian error estimate as an alternative evaluation function and secondly, we show how unordered as well as ordered rules can be generated. We experimentally demonstrate significantly improved performances resulting from these changes, thus enhancing the usefulness of CN2 as an inductive tool. Comparisons with Quinlan's C4.5 are also made. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1138968. 	[Title] Genetic Encoding Strategies for Neural Networks [Abstract] The application of genetic algorithms to neural network optimization (GANN) has produced an active field of research. This paper proposes a classification of the encoding strategies and it also gives a critical analysis of the current state of development. The idea of evolving artificial neural networks (NN) by genetic algorithms (GA) is based on a powerful metaphor: the evolution of the human brain. This mechanism has developed the highest form of intelligence known from scratch. The metaphor has inspired a great deal of research activities that can be traced to the late 1980s (for instance [15]). An increasing amount of research reports, journal papers and theses have been published on the topic, generating a conti-nously growing field. Researchers have devoloped a variety of different techniques to encode neural networks for the GA, with increasing complexity. This young field is driven mostly by small, independet research groups that scarcely cooperate with each other. This paper will attempt to analyse and to structure the already performed work, and to point out the shortcomings of the approaches. 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.

Paper #85449 has the title `A Hypothesis-driven Constructive Induction Approach to Expanding Neural Networks  
` and the 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.
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Question: Which category should Paper #85449 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #85449 has the title `A Hypothesis-driven Constructive Induction Approach to Expanding Neural Networks  
` and the 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.
`.
Question: Which category should Paper #85449 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: Neural_Networks