Above are papers related to the following paper: [Paper_ID] Paper #47684. 	[Title] AN EFFICIENT METRIC FOR HETEROGENEOUS INDUCTIVE LEARNING APPLICATIONS IN THE ATTRIBUTE-VALUE LANGUAGE 1 [Abstract] Many inductive learning problems can be expressed in the classical attribute-value language. In order to learn and to generalize, learning systems often rely on some measure of similarity between their current knowledge base and new information. The attribute-value language defines a heterogeneous multidimensional input space, where some attributes are nominal and others linear. Defining similarity, or proximity, of two points in such input spaces is non trivial. We discuss two representative homogeneous metrics and show examples of why they are limited to their own domains. We then address the issues raised by the design of a heterogeneous metric for inductive learning systems. In particular, we discuss the need for normalization and the impact of don't-care values. We propose a heterogeneous metric and evaluate it empirically on a simplified version of ILA. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #47682. 	[Title] Using Precepts to Augment Training Set Learning an input whose value is don't-care in some [Abstract] are used in turn to approximate A. Empirical studies show that good results can be achieved with TSL [8, 11]. However, TSL has several drawbacks. Training set learners (e.g., backpropagation) are typically slow as they may require many passes over the training set. Also, there is no guarantee that, given an arbitrary training set, the system will find enough good critical features to get a reasonable approximation of A. Moreover, the number of features to be searched is exponential in the number of inputs, and TSL becomes computationally expensive [1]. Finally, the scarcity of interesting positive theoretical results suggests the difficulty of learning without sufficient a priori knowledge. The goal of learning systems is to generalize. Generalization is commonly based on the set of critical features the system has available. Training set learners typically extract critical features from a random set of examples. While this approach is attractive, it suffers from the exponential growth of the number of features to be searched. We propose to extend it by endowing the system with some a priori knowledge, in the form of precepts. Advantages of the augmented system are speedup, improved generalization, and greater parsimony. This paper presents a precept-driven learning algorithm. Its main features include: 1) distributed implementation, 2) bounded learning and execution times, and 3) ability to handle both correct and incorrect precepts. Results of simulations on real-world data demonstrate promise. This paper presents precept-driven learning (PDL). PDL is intended to overcome some of TSL's weaknesses. In PDL, the training set is augmented by a small set of precepts. A pair p = (i, o) in I O is called an example. A precept is an example in which some of the i-entries (inputs) are set to the special value don't-care. An input whose value is not don't-care is said to be asserted. If i has no effect on the value of the output. The use of the special value don't-care is therefore as a shorthand. A pair containing don't-care inputs represents as many examples as the product of the sizes of the input domains of its don't-care inputs. 1. Introduction Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #47683. 	[Title] AN INCREMENTAL LEARNING MODEL FOR COMMONSENSE REASONING [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #424. 	[Title] A Hybrid Nearest-Neighbor and Nearest-Hyperrectangle Algorithm [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #218666. 	[Title] An Implementation and Experiment with the Nested Generalized Exemplars Algorithm [Abstract] This NRL NCARAI technical note (AIC-95-003) describes work with Salzberg's (1991) NGE. I recently implemented this algorithm and have run a few case studies. The purpose of this note is to publicize this implementation and note a curious result while using it. This implementation of NGE is available at under my WWW address Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #197452. 	[Title] Best-First Model Merging for Dynamic Learning and Recognition [Abstract] Best-first model merging is a general technique for dynamically choosing the structure of a neural or related architecture while avoiding overfitting. It is applicable to both learning and recognition tasks and often generalizes significantly better than fixed structures. We demonstrate the approach applied to the tasks of choosing radial basis functions for function learning, choosing local affine models for curve and constraint surface modelling, and choosing the structure of a balltree or bumptree to maximize efficiency of access. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1125393. 	[Title] Beyond predictive accuracy: what? [Abstract] Today's potential users of machine learning technology are faced with the non-trivial problem of choosing, from the large, ever-increasing number of available tools, the one most appropriate for their particular task. To assist the often non-initiated users, it is desirable that this model selection process be automated. Using experience from base level learning, researchers have proposed meta-learning as a possible solution. Historically, predictive accuracy has been the de facto criterion, with most work in meta-learning focusing on the discovery of rules that match applications to models based on accuracy only. Although predictive accuracy is clearly an important criterion, it is also the case that there are a number of other criteria that could, and often ought to, be considered when learning about model selection. This paper presents a number of such criteria and discusses the impact they have on meta-level approaches to model selection. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #68115. 	[Title] ILA: Combining Inductive Learning with Prior Knowledge and Reasoning [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1135125. 	[Title] Constructing Fuzzy Graphs from Examples [Abstract] Methods to build function approximators from example data have gained considerable interest in the past. Especially methodologies that build models that allow an interpretation have attracted attention. Most existing algorithms, however, are either complicated to use or infeasible for high-dimensional problems. This article presents an efficient and easy to use algorithm to construct fuzzy graphs from example data. The resulting fuzzy graphs are based on locally independent fuzzy rules that operate solely on selected, important attributes. This enables the application of these fuzzy graphs also to problems in high dimensional spaces. Using illustrative examples and a real world data set it is demonstrated how the resulting fuzzy graphs offer quick insights into the structure of the example data, that is, the underlying model. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #77758. 	[Title] Parzen. On estimation of a probability density function and mode. Annual Mathematical Statistics, 33:1065-1076, 1962. [Abstract] To apply the algorithm for classification we assign each class a separate set of codebook Gaussians. Each set is only trained with patterns from a single class. After having trained the codebook Gaussians, each set provides an estimate of the probability function of one class; just as with Parzen window estimation, we take as the estimate of the pattern distribution the average of all Gaussians in the set. Classification of a pattern may now be done by calculating the probability of each class at the respective sample point, and assigning to the pattern the class with the highest probability. Hence the whole codebook plays a role in the classification of patterns. This is not the case with regular classification schemes using codebooks. We have tested the classification scheme on several classification tasks including the two spiral problem. We compared our algorithm to various other classification algorithms and it came out second; the best algorithm for the applications is the Parzen window estimation. However, the computing time and memory for Parzen window estimation are excessive when compared to our algorithm, and hence, in practical situations, our algorithm is to be preferred. We have developed a fast algorithm which combines attractive properties of both Parzen window estimation and vector quantization. The scale parameter is tuned adaptively and, therefore, is not set in an ad hoc manner. It allows a classification strategy in which all the codebook vectors are taken into account. This yields better results than the standard vector quantization techniques. An interesting topic for further research is to use radially non-symmetric Gaussians. Please summarise all the given information.

Paper #47684 has the title `AN EFFICIENT METRIC FOR HETEROGENEOUS INDUCTIVE LEARNING APPLICATIONS IN THE ATTRIBUTE-VALUE LANGUAGE 1  
` and the abstract `Many inductive learning problems can be expressed in the classical attribute-value language. In order to learn and to generalize, learning systems often rely on some measure of similarity between their current knowledge base and new information. The attribute-value language defines a heterogeneous multidimensional input space, where some attributes are nominal and others linear. Defining similarity, or proximity, of two points in such input spaces is non trivial. We discuss two representative homogeneous metrics and show examples of why they are limited to their own domains. We then address the issues raised by the design of a heterogeneous metric for inductive learning systems. In particular, we discuss the need for normalization and the impact of don't-care values. We propose a heterogeneous metric and evaluate it empirically on a simplified version of ILA. 
`.
Question: Which category should Paper #47684 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #47684 has the title `AN EFFICIENT METRIC FOR HETEROGENEOUS INDUCTIVE LEARNING APPLICATIONS IN THE ATTRIBUTE-VALUE LANGUAGE 1  
` and the abstract `Many inductive learning problems can be expressed in the classical attribute-value language. In order to learn and to generalize, learning systems often rely on some measure of similarity between their current knowledge base and new information. The attribute-value language defines a heterogeneous multidimensional input space, where some attributes are nominal and others linear. Defining similarity, or proximity, of two points in such input spaces is non trivial. We discuss two representative homogeneous metrics and show examples of why they are limited to their own domains. We then address the issues raised by the design of a heterogeneous metric for inductive learning systems. In particular, we discuss the need for normalization and the impact of don't-care values. We propose a heterogeneous metric and evaluate it empirically on a simplified version of ILA. 
`.
Question: Which category should Paper #47684 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: Case_Based