Above are papers related to the following paper: [Paper_ID] Paper #267003. 	[Title] Integrating Initialization Bias and Search Bias in Neural Network Learning [Abstract] The use of previously learned knowledge during learning has been shown to reduce the number of examples required for good generalization, and to increase robustness to noise in the examples. In reviewing various means of using learned knowledge from a domain to guide further learning in the same domain, two underlying classes are discerned. Methods which use previous knowledge to initialize a learner (as an initialization bias), and those that use previous knowledge to constrain a learner (as a search bias). We show such methods in fact exploit the same domain knowledge differently, and can complement each other. This is shown by presenting a combined approach which both initializes and constrains a learner. This combined approach is seen to outperform the individual methods under the conditions that accurate previously learned domain knowledge is available, and that there are irrelevant features in the domain representation. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #6213. 	[Title] Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction, [Abstract] This article introduces a class of incremental learning procedures specialized for prediction|that is, for using past experience with an incompletely known system to predict its future behavior. Whereas conventional prediction-learning methods assign credit by means of the difference between predicted and actual outcomes, the new methods assign credit by means of the difference between temporally successive predictions. Although such temporal-difference methods have been used in Samuel's checker player, Holland's bucket brigade, and the author's Adaptive Heuristic Critic, they have remained poorly understood. Here we prove their convergence and optimality for special cases and relate them to supervised-learning methods. For most real-world prediction problems, temporal-difference methods require less memory and less peak computation than conventional methods; and they produce more accurate predictions. We argue that most problems to which supervised learning is currently applied are really prediction problems of the sort to which temporal-difference methods can be applied to advantage. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #4649. 	[Title] Finding Accurate Frontiers: A Knowledge-Intensive Approach to Relational Learning [Abstract] An approach to analytic learning is described that searches for accurate entailments of a Horn Clause domain theory. A hill-climbing search, guided by an information based evaluation function, is performed by applying a set of operators that derive frontiers from domain theories. The analytic learning system is one component of a multi-strategy relational learning system. We compare the accuracy of concepts learned with this analytic strategy to concepts learned with an analytic strategy that operationalizes the domain theory. 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.

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 #75121. 	[Title] The Utility of Knowledge in Inductive Learning  Running Head: Knowledge in Inductive Learning [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 #114. 	[Title] Learning to Act using Real-Time Dynamic Programming [Abstract] fl The authors thank Rich Yee, Vijay Gullapalli, Brian Pinette, and Jonathan Bachrach for helping to clarify the relationships between heuristic search and control. We thank Rich Sutton, Chris Watkins, Paul Werbos, and Ron Williams for sharing their fundamental insights into this subject through numerous discussions, and we further thank Rich Sutton for first making us aware of Korf's research and for his very thoughtful comments on the manuscript. We are very grateful to Dimitri Bertsekas and Steven Sullivan for independently pointing out an error in an earlier version of this article. Finally, we thank Harry Klopf, whose insight and persistence encouraged our interest in this class of learning problems. This research was supported by grants to A.G. Barto from the National Science Foundation (ECS-8912623 and ECS-9214866) and the Air Force Office of Scientific Research, Bolling AFB (AFOSR-89-0526). Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1106388. 	[Title] Predicting Ordinal Classes in ILP [Abstract] This paper is devoted to the problem of learning to predict ordinal (i.e., ordered discrete) classes in an ILP setting. We start with a relational regression algorithm named SRT (Structural Regression Trees) and study various ways of transforming it into a first-order learner for ordinal classification tasks. Combinations of these algorithm variants with several data preprocessing methods are compared on two ILP benchmark data sets to verify the relative strengths and weaknesses of the strategies and to study the trade-off between optimal categorical classification accuracy (hit rate) and minimum distance-based error. Preliminary results indicate that this is a promising avenue towards algorithms that combine aspects of classification and regression in relational learning. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #85688. 	[Title] Finding new rules for incomplete theories: Explicit biases for induction with contextual information. In Proceedings [Abstract] addressed in KBANN (which translates a theory into a neural-net, refines it using backpropagation, and then retranslates the result back into rules) by adding extra hidden units and connections to the initial network; however, this would require predetermining the num In this paper, we have presented constructive induction techniques recently added to the EITHER theory refinement system. Intermediate concept utilization employs existing rules in the theory to derive higher-level features for use in induction. Intermediate concept creation employs inverse resolution to introduce new intermediate concepts in order to fill gaps in a theory than span multiple levels. These revisions allow EITHER to make use of imperfect domain theories in the ways typical of previous work in both constructive induction and theory refinement. As a result, EITHER is able to handle a wider range of theory imperfections than any other existing theory refinement system. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #417017. 	[Title] Relating Relational Learning Algorithms [Abstract] Relational learning algorithms are of special interest to members of the machine learning community; they offer practical methods for extending the representations used in algorithms that solve supervised learning tasks. Five approaches are currently being explored to address issues involved with using relational representations. This paper surveys algorithms embodying these approaches, summarizes their empirical evaluations, highlights their commonalities, and suggests potential directions for future research. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1105887. 	[Title] Knowledge Acquisition with a Knowledge-Intensive Machine Learning System [Abstract] In this paper, we investigate the integration of knowledge acquisition and machine learning techniques. We argue that existing machine learning techniques can be made more useful as knowledge acquisition tools by allowing the expert to have greater control over and interaction with the learning process. We describe a number of extensions to FOCL (a multistrategy Horn-clause learning program) that have greatly enhanced its power as a knowledge acquisition tool, paying particular attention to the utility of maintaining a connection between a rule and the set of examples explained by the rule. The objective of this research is to make the modification of a domain theory analogous to the use of a spread sheet. A prototype knowledge acquisition tool, FOCL-1-2-3, has been constructed in order to evaluate the strengths and weaknesses of this approach. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #22431. 	[Title] An investigation of noise-tolerant relational concept learning algorithms [Abstract] We discuss the types of noise that may occur in relational learning systems and describe two approaches to addressing noise in a relational concept learning algorithm. We then evaluate each approach experimentally. Please summarise all the given information.

Paper #267003 has the title `Integrating Initialization Bias and Search Bias in Neural Network Learning  
` and the abstract `The use of previously learned knowledge during learning has been shown to reduce the number of examples required for good generalization, and to increase robustness to noise in the examples. In reviewing various means of using learned knowledge from a domain to guide further learning in the same domain, two underlying classes are discerned. Methods which use previous knowledge to initialize a learner (as an initialization bias), and those that use previous knowledge to constrain a learner (as a search bias). We show such methods in fact exploit the same domain knowledge differently, and can complement each other. This is shown by presenting a combined approach which both initializes and constrains a learner. This combined approach is seen to outperform the individual methods under the conditions that accurate previously learned domain knowledge is available, and that there are irrelevant features in the domain representation. 
`.
Question: Which category should Paper #267003 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #267003 has the title `Integrating Initialization Bias and Search Bias in Neural Network Learning  
` and the abstract `The use of previously learned knowledge during learning has been shown to reduce the number of examples required for good generalization, and to increase robustness to noise in the examples. In reviewing various means of using learned knowledge from a domain to guide further learning in the same domain, two underlying classes are discerned. Methods which use previous knowledge to initialize a learner (as an initialization bias), and those that use previous knowledge to constrain a learner (as a search bias). We show such methods in fact exploit the same domain knowledge differently, and can complement each other. This is shown by presenting a combined approach which both initializes and constrains a learner. This combined approach is seen to outperform the individual methods under the conditions that accurate previously learned domain knowledge is available, and that there are irrelevant features in the domain representation. 
`.
Question: Which category should Paper #267003 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