Above are papers related to the following paper: [Paper_ID] Paper #46452. 	[Title] Incremental Reduced Error Pruning [Abstract] This paper outlines some problems that may occur with Reduced Error Pruning in Inductive Logic Programming, most notably efficiency. Thereafter a new method, Incremental Reduced Error Pruning, is proposed that attempts to address all of these problems. Experiments show that in many noisy domains this method is much more efficient than alternative algorithms, along with a slight gain in accuracy. However, the experiments show as well that the use of this algorithm cannot be recommended for domains with a very specific concept description. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #25794. 	[Title] Is Learning The n-th Thing Any Easier Than Learning The First? [Abstract] This paper investigates learning in a lifelong context. Lifelong learning addresses situations in which a learner faces a whole stream of learning tasks. Such scenarios provide the opportunity to transfer knowledge across multiple learning tasks, in order to generalize more accurately from less training data. In this paper, several different approaches to lifelong learning are described, and applied in an object recognition domain. It is shown that across the board, lifelong learning approaches generalize consistently more accurately from less training data, by their ability to transfer knowledge across learning tasks. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1153091. 	[Title] More Efficient Windowing [Abstract] Windowing has been proposed as a procedure for efficient memory use in the ID3 decision tree learning algorithm. However, previous work has shown that windowing may often lead to a decrease in performance. In this work, we try to argue that separate-and-conquer rule learning algorithms are more appropriate for windowing than divide-and-conquer algorithms, because they learn rules independently and are less susceptible to changes in class distributions. In particular, we will present a new windowing algorithm that achieves additional gains in efficiency by exploiting this property of separate-and-conquer algorithms. While the presented algorithm is only suitable for redundant, noise-free data sets, we will also briefly discuss the problem of noisy data in windowing and present some preliminary ideas how it might be solved with an extension of the algorithm introduced in this paper. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #582343. 	[Title] Transferring and Retraining Learned Information Filters [Abstract] Any system that learns how to filter documents will suffer poor performance during an initial training phase. One way of addressing this problem is to exploit filters learned by other users in a collaborative fashion. We investigate "direct transfer" of learned filters in this setting|a limiting case for any collaborative learning system. We evaluate the stability of several different learning methods under direct transfer, and conclude that symbolic learning methods that use negatively correlated features of the data perform poorly in transfer, even when they perform well in more conventional evaluation settings. This effect is robust: it holds for several learning methods, when a diverse set of users is used in training the classifier, and even when the learned classifiers can be adapted to the new user's distribution. Our experiments give rise to several concrete proposals for improving generalization performance in a collaborative setting, including a beneficial variation on a feature selection method that has been widely used in text categorization. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #2440. 	[Title] Quinlan, 1990 J.R. Quinlan. Learning logical definitions from relations. Machine Learning, First-order theory revision. In [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #4660. 	[Title] Context-sensitive learning methods for text categorization [Abstract] Two recently implemented machine learning algorithms, RIPPER and sleeping experts for phrases, are evaluated on a number of large text categorization problems. These algorithms both construct classifiers that allow the "context" of a word w to affect how (or even whether) the presence or absence of w will contribute to a classification. However, RIPPER and sleeping experts differ radically in many other respects: differences include different notions as to what constitutes a context, different ways of combining contexts to construct a classifier, different methods to search for a combination of contexts, and different criteria as to what contexts should be included in such a combination. In spite of these differences, both RIPPER and sleeping experts perform extremely well across a wide variety of categorization problems, generally outperforming previously applied learning methods. We view this result as a confirmation of the usefulness of classifiers that represent contextual information. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1128437. 	[Title] Feature Generation for Sequence Categorization [Abstract] The problem of sequence categorization is to generalize from a corpus of labeled sequences procedures for accurately labeling future unlabeled sequences. The choice of representation of sequences can have a major impact on this task, and in the absence of background knowledge a good representation is often not known and straightforward representations are often far from optimal. We propose a feature generation method (called FGEN) that creates Boolean features that check for the presence or absence of heuristically selected collections of subsequences. We show empirically that the representation computed by FGEN improves the accuracy of two commonly used learning systems (C4.5 and Ripper) when the new features are added to existing representations of sequence data. We show the superiority of FGEN across a range of tasks selected from three domains: DNA sequences, Unix command sequences, and English text. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #18615. 	[Title] Learning Trees and Rules with Set-valued Features [Abstract] In most learning systems examples are represented as fixed-length "feature vectors", the components of which are either real numbers or nominal values. We propose an extension of the feature-vector representation that allows the value of a feature to be a set of strings; for instance, to represent a small white and black dog with the nominal features size and species and the set-valued feature color, one might use a feature vector with size=small, species=canis-familiaris and color=fwhite,blackg. Since we make no assumptions about the number of possible set elements, this extension of the traditional feature-vector representation is closely connected to Blum's "infinite attribute" representation. We argue that many decision tree and rule learning algorithms can be easily extended to set-valued features. We also show by example that many real-world learning problems can be efficiently and naturally represented with set-valued features; in particular, text categorization problems and problems that arise in propositionalizing first-order representations lend themselves to set-valued features. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #18619. 	[Title] Heterogeneous Uncertainty Sampling for Supervised Learning [Abstract] Uncertainty sampling methods iteratively request class labels for training instances whose classes are uncertain despite the previous labeled instances. These methods can greatly reduce the number of instances that an expert need label. One problem with this approach is that the classifier best suited for an application may be too expensive to train or use during the selection of instances. We test the use of one classifier (a highly efficient probabilistic one) to select examples for training another (the C4.5 rule induction program). Despite being chosen by this heterogeneous approach, the uncertainty samples yielded classifiers with lower error rates than random samples ten times larger. Please summarise all the given information.

Paper #46452 has the title `Incremental Reduced Error Pruning  
` and the abstract `This paper outlines some problems that may occur with Reduced Error Pruning in Inductive Logic Programming, most notably efficiency. Thereafter a new method, Incremental Reduced Error Pruning, is proposed that attempts to address all of these problems. Experiments show that in many noisy domains this method is much more efficient than alternative algorithms, along with a slight gain in accuracy. However, the experiments show as well that the use of this algorithm cannot be recommended for domains with a very specific concept description. 
`.
Question: Which category should Paper #46452 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #46452 has the title `Incremental Reduced Error Pruning  
` and the abstract `This paper outlines some problems that may occur with Reduced Error Pruning in Inductive Logic Programming, most notably efficiency. Thereafter a new method, Incremental Reduced Error Pruning, is proposed that attempts to address all of these problems. Experiments show that in many noisy domains this method is much more efficient than alternative algorithms, along with a slight gain in accuracy. However, the experiments show as well that the use of this algorithm cannot be recommended for domains with a very specific concept description. 
`.
Question: Which category should Paper #46452 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