Above are papers related to the following paper: [Paper_ID] Paper #83461. 	[Title] Dlab: A Declarative Language Bias Formalism [Abstract] We describe the principles and functionalities of Dlab (Declarative LAnguage Bias). Dlab can be used in inductive learning systems to define syntactically and traverse efficiently finite subspaces of first order clausal logic, be it a set of propositional formulae, association rules, Horn clauses, or full clauses. A Prolog implementation of Dlab is available by ftp access. Keywords: declarative language bias, concept learning, knowledge dis covery Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1155073. 	[Title] Learning with Abduction [Abstract] We investigate how abduction and induction can be integrated into a common learning framework through the notion of Abductive Concept Learning (ACL). ACL is an extension of Inductive Logic Programming (ILP) to the case in which both the background and the target theory are abductive logic programs and where an abductive notion of entailment is used as the coverage relation. In this framework, it is then possible to learn with incomplete information about the examples by exploiting the hypothetical reasoning of abduction. The paper presents the basic framework of ACL with its main characteristics and illustrates its potential in addressing several problems in ILP such as learning with incomplete information and multiple predicate learning. An algorithm for ACL is developed by suitably extending the top-down ILP method for concept learning and integrating this with an abductive proof procedure for Abductive Logic Programming (ALP). A prototype system has been developed and applied to learning problems with incomplete information. The particular role of integrity constraints in ACL is investigated showing ACL as a hybrid learning framework that integrates the explanatory (discriminant) and descriptive (characteristic) settings of ILP. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #509379. 	[Title] Model selection using measure functions [Abstract] The concept of measure functions for generalization performance is suggested. This concept provides an alternative way of selecting and evaluating learned models (classifiers). In addition, it makes it possible to state a learning problem as a computational problem. The the known prior (meta-)knowledge about the problem domain is captured in a measure function that, to each possible combination of a training set and a classifier, assigns a value describing how good the classifier is. The computational problem is then to find a classifier maximizing the measure function. We argue that measure functions are of great value for practical applications. Besides of being a tool for model selection, they: (i) force us to make explicit the relevant prior knowledge about the learning problem at hand, (ii) provide a deeper understanding of existing algorithms, and (iii) help us in the construction of problem-specific algorithms. We illustrate the last point by suggesting a novel algorithm based on incremental search for a classifier that optimizes a given measure function. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #144330. 	[Title] Utilizing Prior Concepts for Learning [Abstract] The inductive learning problem consists of learning a concept given examples and non-examples of the concept. To perform this learning task, inductive learning algorithms bias their learning method. Here we discuss biasing the learning method to use previously learned concepts from the same domain. These learned concepts highlight useful information for other concepts in the domain. We describe a transference bias and present M-FOCL, a Horn clause relational learning algorithm, that utilizes this bias to learn multiple concepts. We provide preliminary empirical evaluation to show the effects of biasing previous information on noise-free and noisy data. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #954315. 	[Title] Inductive Database Design [Abstract] When designing a (deductive) database, the designer has to decide for each predicate (or relation) whether it should be defined extensionally or intensionally, and what the definition should look like. An intelligent system is presented to assist the designer in this task. It starts from an example database in which all predicates are defined extensionally. It then tries to compact the database by transforming extensionally defined predicates into intensionally defined ones. The intelligent system employs techniques from the area of inductive logic programming. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #72908. 	[Title] Applications of a logical discovery engine [Abstract] The clausal discovery engine claudien is presented. claudien discovers regularities in data and is a representative of the inductive logic programming paradigm. As such, it represents data and regularities by means of first order clausal theories. Because the search space of clausal theories is larger than that of attribute value representation, claudien also accepts as input a declarative specification of the language bias, which determines the set of syntactically well-formed regularities. Whereas other papers on claudien focuss on the semantics or logical problem specification of claudien, on the discovery algorithm, or the PAC-learning aspects, this paper wants to illustrate the power of the resulting technique. In order to achieve this aim, we show how claudien can be used to learn 1) integrity constraints in databases, 2) functional dependencies and determinations, 3) properties of sequences, 4) mixed quantitative and qualitative laws, 5) reverse engineering, and 6) classification rules. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #96845. 	[Title] Evolution-based Discovery of Hierarchical Behaviors [Abstract] Procedural representations of control policies have two advantages when facing the scale-up problem in learning tasks. First they are implicit, with potential for inductive generalization over a very large set of situations. Second they facilitate modularization. In this paper we compare several randomized algorithms for learning modular procedural representations. The main algorithm, called Adaptive Representation through Learning (ARL) is a genetic programming extension that relies on the discovery of subroutines. ARL is suitable for learning hierarchies of subroutines and for constructing policies to complex tasks. ARL was successfully tested on a typical reinforcement learning problem of controlling an agent in a dynamic and nondeterministic environment where the discovered subroutines correspond to agent behaviors. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #56115. 	[Title] Improving Tactical Plans with Genetic Algorithms [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #17811. 	[Title] #1 Robust Feature Selection Algorithms [Abstract] Selecting a set of features which is optimal for a given task is a problem which plays an important role in a wide variety of contexts including pattern recognition, adaptive control, and machine learning. Our experience with traditional feature selection algorithms in the domain of machine learning lead to an appreciation for their computational efficiency and a concern for their brittleness. This paper describes an alternate approach to feature selection which uses genetic algorithms as the primary search component. Results are presented which suggest that genetic algorithms can be used to increase the robustness of feature selection algorithms without a significant decrease in computational efficiency. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #10169. 	[Title] Learning Boolean Concepts in the Presence of Many Irrelevant Features [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #63486. 	[Title] Evaluation and Selection of Biases in Machine Learning [Abstract] In this introduction, we define the term bias as it is used in machine learning systems. We motivate the importance of automated methods for evaluating and selecting biases using a framework of bias selection as search in bias and meta-bias spaces. Recent research in the field of machine learning bias is summarized. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #3231. 	[Title] Irrelevant Features and the Subset Selection Problem [Abstract] We address the problem of finding a subset of features that allows a supervised induction algorithm to induce small high-accuracy concepts. We examine notions of relevance and irrelevance, and show that the definitions used in the machine learning literature do not adequately partition the features into useful categories of relevance. We present definitions for irrelevance and for two degrees of relevance. These definitions improve our understanding of the behavior of previous subset selection algorithms, and help define the subset of features that should be sought. The features selected should depend not only on the features and the target concept, but also on the induction algorithm. We describe a method for feature subset selection using cross-validation that is applicable to any induction algorithm, and discuss experiments conducted with ID3 and C4.5 on artificial and real datasets. Please summarise all the given information.

Paper #83461 has the title `Dlab: A Declarative Language Bias Formalism  
` and the abstract `We describe the principles and functionalities of Dlab (Declarative LAnguage Bias). Dlab can be used in inductive learning systems to define syntactically and traverse efficiently finite subspaces of first order clausal logic, be it a set of propositional formulae, association rules, Horn clauses, or full clauses. A Prolog implementation of Dlab is available by ftp access. Keywords: declarative language bias, concept learning, knowledge dis covery
`.
Question: Which category should Paper #83461 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #83461 has the title `Dlab: A Declarative Language Bias Formalism  
` and the abstract `We describe the principles and functionalities of Dlab (Declarative LAnguage Bias). Dlab can be used in inductive learning systems to define syntactically and traverse efficiently finite subspaces of first order clausal logic, be it a set of propositional formulae, association rules, Horn clauses, or full clauses. A Prolog implementation of Dlab is available by ftp access. Keywords: declarative language bias, concept learning, knowledge dis covery
`.
Question: Which category should Paper #83461 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