Above are papers related to the following paper: [Paper_ID] Paper #1130634. 	[Title] Protein Secondary Structure Modelling with Probabilistic Networks (Extended Abstract) [Abstract] In this paper we study the performance of probabilistic networks in the context of protein sequence analysis in molecular biology. Specifically, we report the results of our initial experiments applying this framework to the problem of protein secondary structure prediction. One of the main advantages of the probabilistic approach we describe here is our ability to perform detailed experiments where we can experiment with different models. We can easily perform local substitutions (mutations) and measure (probabilistically) their effect on the global structure. Window-based methods do not support such experimentation as readily. Our method is efficient both during training and during prediction, which is important in order to be able to perform many experiments with different networks. We believe that probabilistic methods are comparable to other methods in prediction quality. In addition, the predictions generated by our methods have precise quantitative semantics which is not shared by other classification methods. Specifically, all the causal and statistical independence assumptions are made explicit in our networks thereby allowing biologists to study and experiment with different causal models in a convenient manner. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #8832. 	[Title] The Possible Contribution of AI to the Avoidance of Crises and Wars: Using CBR Methods [Abstract] This paper presents the application of Case-Based Reasoning methods to the KOSIMO data base of international conflicts. A Case-Based Reasoning tool - VIE-CBR has been deveolped and used for the classification of various outcome variables, like political, military, and territorial outcome, solution modalities, and conflict intensity. In addition, the case retrieval algorithms are presented as an interactive, user-modifiable tool for intelli gently searching the conflict data base for precedent cases. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1102850. 	[Title] Optimal Alignments in Linear Space using Automaton-derived Cost Functions (Extended Abstract) Submitted to CPM'96 [Abstract] In a previous paper [SM95], we showed how finite automata could be used to define objective functions for assessing the quality of an alignment of two (or more) sequences. In this paper, we show some results of using such cost functions. We also show how to extend Hischberg's linear space algorithm [Hir75] to this setting, thus generalizing a result of Myers and Miller [MM88b]. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #346243. 	[Title] Inductive Bias in Case-Based Reasoning Systems [Abstract] In order to learn more about the behaviour of case-based reasoners as learning systems, we form-alise a simple case-based learner as a PAC learning algorithm, using the case-based representation hCB; i. We first consider a `naive' case-based learning algorithm CB1( H ) which learns by collecting all available cases into the case-base and which calculates similarity by counting the number of features on which two problem descriptions agree. We present results concerning the consistency of this learning algorithm and give some partial results regarding its sample complexity. We are able to characterise CB1( H ) as a `weak but general' learning algorithm. We then consider how the sample complexity of case-based learning can be reduced for specific classes of target concept by the application of inductive bias, or prior knowledge of the class of target concepts. Following recent work demonstrating how case-based learning can be improved by choosing a similarity measure appropriate to the concept being learnt, we define a second case-based learning `algorithm' CB2 which learns using the best possible similarity measure that might be inferred for the chosen target concept. While CB2 is not an executable learning strategy (since the chosen similarity measure is defined in terms of a priori knowledge of the actual target concept) it allows us to assess in the limit the maximum possible contribution of this approach to case-based learning. Also, in addition to illustrating the role of inductive bias, the definition of CB2 simplifies the general problem of establishing which functions might be represented in the form hCB; i. Reasoning about the case-based representation in this special case has therefore been a little more straight-forward than in the general case of CB1( H ), allowing more substantial results regarding representable functions and sample complexity to be presented for CB2. In assessing these results, we are forced to conclude that case-based learning is not the best approach to learning the chosen concept space (the space of monomial functions). We discuss, however, how our study has demonstrated, in the context of case-based learning, the operation of concepts well known in machine learning such as inductive bias and the trade-off between computational complexity and sample complexity. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #36620. 	[Title] Towards a Better Understanding of Memory-Based Reasoning Systems [Abstract] We quantify both experimentally and analytically the performance of memory-based reasoning (MBR) algorithms. To start gaining insight into the capabilities of MBR algorithms, we compare an MBR algorithm using a value difference metric to a popular Bayesian classifier. These two approaches are similar in that they both make certain independence assumptions about the data. However, whereas MBR uses specific cases to perform classification, Bayesian methods summarize the data probabilistically. We demonstrate that a particular MBR system called Pebls works comparatively well on a wide range of domains using both real and artificial data. With respect to the artificial data, we consider distributions where the concept classes are separated by functional discriminants, as well as time-series data generated by Markov models of varying complexity. Finally, we show formally that Pebls can learn (in the limit) natural concept classes that the Bayesian classifier cannot learn, and that it will attain perfect accuracy whenever Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #3220. 	[Title] A Weighted Nearest Neighbor Algorithm for Learning with Symbolic Features [Abstract] In the past, nearest neighbor algorithms for learning from examples have worked best in domains in which all features had numeric values. In such domains, the examples can be treated as points and distance metrics can use standard definitions. In symbolic domains, a more sophisticated treatment of the feature space is required. We introduce a nearest neighbor algorithm for learning in domains with symbolic features. Our algorithm calculates distance tables that allow it to produce real-valued distances between instances, and attaches weights to the instances to further modify the structure of feature space. We show that this technique produces excellent classification accuracy on three problems that have been studied by machine learning researchers: predicting protein secondary structure, identifying DNA promoter sequences, and pronouncing English text. Direct experimental comparisons with the other learning algorithms show that our nearest neighbor algorithm is comparable or superior in all three domains. In addition, our algorithm has advantages in training speed, simplicity, and perspicuity. We conclude that experimental evidence favors the use and continued development of nearest neighbor algorithms for domains such as the ones studied here. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #69397. 	[Title] Dynamical Selection of Learning Algorithms [Abstract] Determining the conditions for which a given learning algorithm is appropriate is an open problem in machine learning. Methods for selecting a learning algorithm for a given domain have met with limited success. This paper proposes a new approach to predicting a given example's class by locating it in the "example space" and then choosing the best learner(s) in that region of the example space to make predictions. The regions of the example space are defined by the prediction patterns of the learners being used. The learner(s) chosen for prediction are selected according to their past performance in that region. This dynamic approach to learning algorithm selection is compared to other methods for selecting from multiple learning algorithms. The approach is then extended to weight rather than select the algorithms according to their past performance in a given region. Both approaches are further evaluated on a set of Determining the conditions for which a given learning algorithm is appropriate is an open problem in machine learning. Methods for selecting a learning algorithm for a given domain (e.g. [Aha92, Breiman84]) or for a portion of the domain ([Brodley93, Brodley94]) have met with limited success. This paper proposes a new approach that dynamically selects a learning algorithm for each example by locating it in the "example space" and then choosing the best learner(s) for prediction in that part of the example space. The regions of the example space are formed by the observed prediction patterns of the learners being used. The learner(s) chosen for prediction are selected according to their past performance in that region which is defined by the "cross-validation history." This paper introduces DS, a method for the dynamic selection of a learning algorithm(s). We call it "dynamic" because the learning algorithm(s) used to classify a novel example depends on that example. Preliminary experimentation motivated DW, an extension to DS that dynamically weights the learners predictions according to their regional accuracy. Further experimentation compares DS and DW to a collection of other meta-learning strategies such as cross-validation ([Breiman84]) and various forms of stacking ([Wolpert92]). In this phase of the experiementation, the meta-learners have six constituent learners which are heterogeneous in their search and representation methods (e.g. a rule learner, CN2 [Clark89]; a decision tree learner, C4.5 [Quinlan93]; an oblique decision tree learner, OC1 [Murthy93]; an instance-based learner, PEBLS [Cost93]; a k-nearest neighbor learner, ten domains and compared to several other meta-learning strategies. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1115677. 	[Title] Comparison of Regression Methods, Symbolic Induction Methods and Neural Networks in Morbidity Diagnosis and Mortality [Abstract] Classifier induction algorithms differ on what inductive hypotheses they can represent, and on how they search their space of hypotheses. No classifier is better than another for all problems: they have selective superiority. This paper empirically compares six classifier induction algorithms on the diagnosis of equine colic and the prediction of its mortality. The classification is based on simultaneously analyzing sixteen features measured from a patient. The relative merits of the algorithms (linear regression, decision trees, nearest neighbor classifiers, the Model Class Selection system, logistic regression (with and without feature selection), and neural nets) are qualitatively discussed, and the generalization accuracies quantitatively analyzed. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1132706. 	[Title] Exemplar-based Music Structure Recognition [Abstract] We tend to think of what we really know as what we can talk about, and disparage knowledge that we can't verbalize. [Dowling 1989, p. 252] Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #22563. 	[Title] A Comparative Study of ID3 and Backpropagation for English Text-to-Speech Mapping [Abstract] The performance of the error backpropagation (BP) and ID3 learning algorithms was compared on the task of mapping English text to phonemes and stresses. Under the distributed output code developed by Sejnowski and Rosenberg, it is shown that BP consistently out-performs ID3 on this task by several percentage points. Three hypotheses explaining this difference were explored: (a) ID3 is overfitting the training data, (b) BP is able to share hidden units across several output units and hence can learn the output units better, and (c) BP captures statistical information that ID3 does not. We conclude that only hypothesis (c) is correct. By augmenting ID3 with a simple statistical learning procedure, the performance of BP can be approached but not matched. More complex statistical procedures can improve the performance of both BP and ID3 substantially. A study of the residual errors suggests that there is still substantial room for improvement in learning methods for text-to-speech mapping. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #10531. 	[Title] Using Dirichlet Mixture Priors to Derive Hidden Markov Models for Protein Families [Abstract] A Bayesian method for estimating the amino acid distributions in the states of a hidden Markov model (HMM) for a protein family or the columns of a multiple alignment of that family is introduced. This method uses Dirichlet mixture densities as priors over amino acid distributions. These mixture densities are determined from examination of previously constructed HMMs or multiple alignments. It is shown that this Bayesian method can improve the quality of HMMs produced from small training sets. Specific experiments on the EF-hand motif are reported, for which these priors are shown to produce HMMs with higher likelihood on unseen data, and fewer false positives and false negatives in a database search task. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #43698. 	[Title] Dirichlet Mixtures: A Method for Improving Detection of Weak but Significant Protein Sequence Homology [Abstract] UCSC Technical Report UCSC-CRL-96-09 Abstract This paper presents the mathematical foundations of Dirichlet mixtures, which have been used to improve database search results for homologous sequences, when a variable number of sequences from a protein family or domain are known. We present a method for condensing the information in a protein database into a mixture of Dirichlet densities. These mixtures are designed to be combined with observed amino acid frequencies, to form estimates of expected amino acid probabilities at each position in a profile, hidden Markov model, or other statistical model. These estimates give a statistical model greater generalization capacity, such that remotely related family members can be more reliably recognized by the model. Dirichlet mixtures have been shown to outperform substitution matrices and other methods for computing these expected amino acid distributions in database search, resulting in fewer false positives and false negatives for the families tested. This paper corrects a previously published formula for estimating these expected probabilities, and contains complete derivations of the Dirichlet mixture formulas, methods for optimizing the mixtures to match particular databases, and suggestions for efficient implementation. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #194617. 	[Title] Finding Genes in DNA with a Hidden Markov Model [Abstract] This study describes a new Hidden Markov Model (HMM) system for segmenting uncharacterized genomic DNA sequences into exons, introns, and intergenic regions. Separate HMM modules were designed and trained for specific regions of DNA: exons, introns, intergenic regions, and splice sites. The models were then tied together to form a biologically feasible topology. The integrated HMM was trained further on a set of eukaryotic DNA sequences, and tested by using it to segment a separate set of sequences. The resulting HMM system, which is called VEIL (Viterbi Exon-Intron Locator), obtains an overall accuracy on test data of 92% of total bases correctly labelled, with a correlation coefficient of 0.73. Using the more stringent test of exact exon prediction, VEIL correctly located both ends of 53% of the coding exons, and 49% of the exons it predicts are exactly correct. These results compare favorably to the best previous results for gene structure prediction, and demonstrate the benefits of using HMMs for this problem. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #145215. 	[Title] Memory-Based Lexical Acquisition and Processing [Abstract] Current approaches to computational lexicology in language technology are knowledge-based (competence-oriented) and try to abstract away from specific formalisms, domains, and applications. This results in severe complexity, acquisition and reusability bottlenecks. As an alternative, we propose a particular performance-oriented approach to Natural Language Processing based on automatic memory-based learning of linguistic (lexical) tasks. The consequences of the approach for computational lexicology are discussed, and the application of the approach on a number of lexical acquisition and disambiguation tasks in phonology, morphology and syntax is described. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #686532. 	[Title] Homology Detection via Family Pairwise Search a straightforward generalization of pairwise sequence comparison algorithms to [Abstract] The function of an unknown biological sequence can often be accurately inferred by identifying sequences homologous to the original sequence. Given a query set of known homologs, there exist at least three general classes of techniques for finding additional homologs: pairwise sequence comparisons, motif analysis, and hidden Markov modeling. Pairwise sequence comparisons are typically employed when only a single query sequence is known. Hidden Markov models (HMMs), on the other hand, are usually trained with sets of more than 100 sequences. Motif-based methods fall in between these two extremes. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1131728. 	[Title] Fast NP Chunking Using Memory-Based Learning Techniques [Abstract] In this paper we discuss the application of Memory-Based Learning (MBL) to fast NP chunking. We first discuss the application of a fast decision tree variant of MBL (IGTree) on the dataset described in (Ramshaw and Marcus, 1995), which consists of roughly 50,000 test and 200,000 train items. In a second series of experiments we used an architecture of two cascaded IGTrees. In the second level of this cascaded classifier we added context predictions as extra features so that incorrect predictions from the first level can be corrected, yielding a 97.2% generalisation accuracy with training and testing times in the order of seconds to minutes. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #39124. 	[Title] The Utility of Feature Weighting in Nearest-Neighbor Algorithms [Abstract] Nearest-neighbor algorithms are known to depend heavily on their distance metric. In this paper, we investigate the use of a weighted Euclidean metric in which the weight for each feature comes from a small set of options. We describe Diet, an algorithm that directs search through a space of discrete weights using cross-validation error as its evaluation function. Although a large set of possible weights can reduce the learner's bias, it can also lead to increased variance and overfitting. Our empirical study shows that, for many data sets, there is an advantage to weighting features, but that increasing the number of possible weights beyond two (zero and one) has very little benefit and sometimes degrades performance. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #40922. 	[Title] An Optimal Weighting Criterion of Case Indexing for Both Numeric and Symbolic Attributes [Abstract] Indexing of cases is an important topic for Memory-Based Reasoning(MBR). One key problem is how to assign weights to attributes of cases. Although several weighting methods have been proposed, some methods cannot handle numeric attributes directly, so it is necessary to discretize numeric values by classification. Furthermore, existing methods have no theoretical background, so little can be said about optimality. We propose a new weighting method based on a statistical technique called Quantification Method II. It can handle both numeric and symbolic attributes in the same framework. Generated attribute weights are optimal in the sense that they maximize the ratio of variance between classes to variance of all cases. Experiments on several benchmark tests show that in many cases, our method obtains higher accuracies than some other weighting methods. The results also indicate that it can distinguish relevant attributes from irrelevant ones, and can tolerate noisy data. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #654177. 	[Title] RAPID DEVELOPMENT OF NLP MODULES WITH MEMORY-BASED LEARNING [Abstract] The need for software modules performing natural language processing (NLP) tasks is growing. These modules should perform efficiently and accurately, while at the same time rapid development is often mandatory. Recent work has indicated that machine learning techniques in general, and memory-based learning (MBL) in particular, offer the tools to meet both ends. We present examples of modules trained with MBL on three NLP tasks: (i) text-to-speech conversion, (ii) part-of-speech tagging, and (iii) phrase chunking. We demonstrate that the three modules display high generalization accuracy, and argue why MBL is applicable similarly well to a large class of other NLP tasks. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1129442. 	[Title] Minimum-Risk Profiles of Protein Families Based on Statistical Decision Theory [Abstract] Statistical decision theory provides a principled way to estimate amino acid frequencies in conserved positions of a protein family. The goal is to minimize the risk function, or the expected squared-error distance between the estimates and the true population frequencies. The minimum-risk estimates are obtained by adding an optimal number of pseudocounts to the observed data. Two formulas are presented, one for pseudocounts based on marginal amino acid frequencies and one for pseudocounts based on the observed data. Experimental results show that profiles constructed using minimal-risk estimates are more discriminating than those constructed using existing methods. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #120039. 	[Title] EXPLORING A FRAMEWORK FOR INSTANCE BASED LEARNING AND NAIVE BAYESIAN CLASSIFIERS [Abstract] The relative performance of different methods for classifier learning varies across domains. Some recent Instance Based Learning (IBL) methods, such as IB1-MVDM* 10 , use similarity measures based on conditional class probabilities. These probabilities are a key component of Naive Bayes methods. Given this commonality of approach, it is of interest to consider how the differences between the two methods are linked to their relative performance in different domains. Here we interpret Naive Bayes in an IBL like framework, identifying differences between Naive Bayes and IB1-MVDM* in this framework. Experiments on variants of IB1-MVDM* that lie between it and Naive Bayes in the framework are conducted on sixteen domains. The results strongly suggest that the relative performance of Naive Bayes and IB1-MVDM* is linked to the extent to which each class can be satisfactorily represented by a single instance in the IBL framework. However, this is not the only factor that appears significant. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1125992. 	[Title] Towards a Theory of Optimal Similarity Measures way of learning a similarity measure from the [Abstract] The effectiveness of a case-based reasoning system is known to depend critically on its similarity measure. However, it is not clear whether there are elusive and esoteric similarity measures which might improve the performance of a case-based reasoner if substituted for the more commonly used measures. This paper therefore deals with the problem of choosing the best similarity measure, in the limited context of instance-based learning of classifications of a discrete example space. We consider both `fixed' similarity measures and `learnt' ones. In the former case, we give a definition of a similarity measure which we believe to be `optimal' w.r.t. the current prior distribution of target concepts and prove its optimality within a restricted class of similarity measures. We then show how this `optimal' similarity measure is instantiated by some specific prior distributions, and conclude that a very simple similarity measure is as good as any other in these cases. In a further section, we then show how our definition leads naturally to a conjecture about the Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #31336. 	[Title] The megaprior heuristic for discovering protein sequence patterns [Abstract] Several computer algorithms for discovering patterns in groups of protein sequences are in use that are based on fitting the parameters of a statistical model to a group of related sequences. These include hidden Markov model (HMM) algorithms for multiple sequence alignment, and the MEME and Gibbs sampler algorithms for discovering motifs. These algorithms are sometimes prone to producing models that are incorrect because two or more patterns have been combined. The statistical model produced in this situation is a convex combination (weighted average) of two or more different models. This paper presents a solution to the problem of convex combinations in the form of a heuristic based on using extremely low variance Dirichlet mixture priors as part of the statistical model. This heuristic, which we call the megaprior heuristic, increases the strength (i.e., decreases the variance) of the prior in proportion to the size of the sequence dataset. This causes each column in the final model to strongly resemble the mean of a single component of the prior, regardless of the size of the dataset. We describe the cause of the convex combination problem, analyze it mathematically, motivate and describe the implementation of the megaprior heuristic, and show how it can effectively eliminate the problem of convex combinations in protein sequence pattern discovery. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #429805. 	[Title] Resolving PP attachment Ambiguities with Memory-Based Learning [Abstract] In this paper we describe the application of Memory-Based Learning to the problem of Prepositional Phrase attachment disambiguation. We compare Memory-Based Learning, which stores examples in memory and generalizes by using intelligent similarity metrics, with a number of recently proposed statistical methods that are well suited to large numbers of features. We evaluate our methods on a common benchmark dataset and show that our method compares favorably to previous methods, and is well-suited to incorporating various unconventional representations of word patterns such as value difference metrics and Lexical Space. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1128430. 	[Title] On Reasoning from Data [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1107567. 	[Title] Learning Algorithms with Applications to Robot Navigation and Protein Folding [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #31349. 	[Title] Meta-MEME: Motif-based Hidden Markov Models of Protein Families [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #167670. 	[Title] Error-Based and Entropy-Based Discretization of Continuous Features [Abstract] We present a comparison of error-based and entropy-based methods for discretization of continuous features. Our study includes both an extensive empirical comparison as well as an analysis of scenarios where error minimization may be an inappropriate discretization criterion. We present a discretization method based on the C4.5 decision tree algorithm and compare it to an existing entropy-based discretization algorithm, which employs the Minimum Description Length Principle, and a recently proposed error-based technique. We evaluate these discretization methods with respect to C4.5 and Naive-Bayesian classifiers on datasets from the UCI repository and analyze the computational complexity of each method. Our results indicate that the entropy-based MDL heuristic outperforms error minimization on average. We then analyze the shortcomings of error-based approaches in comparison to entropy-based methods. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #31353. 	[Title] Hidden Markov Models in Computational Biology: Applications to Protein Modeling UCSC-CRL-93-32 Keywords: Hidden Markov Models, [Abstract] Hidden Markov Models (HMMs) are applied to the problems of statistical modeling, database searching and multiple sequence alignment of protein families and protein domains. These methods are demonstrated on the globin family, the protein kinase catalytic domain, and the EF-hand calcium binding motif. In each case the parameters of an HMM are estimated from a training set of unaligned sequences. After the HMM is built, it is used to obtain a multiple alignment of all the training sequences. It is also used to search the SWISS-PROT 22 database for other sequences that are members of the given protein family, or contain the given domain. The HMM produces multiple alignments of good quality that agree closely with the alignments produced by programs that incorporate three-dimensional structural information. When employed in discrimination tests (by examining how closely the sequences in a database fit the globin, kinase and EF-hand HMMs), the HMM is able to distinguish members of these families from non-members with a high degree of accuracy. Both the HMM and PRO-FILESEARCH (a technique used to search for relationships between a protein sequence and multiply aligned sequences) perform better in these tests than PROSITE (a dictionary of sites and patterns in proteins). The HMM appears to have a slight advantage Please summarise all the given information.

Paper #1130634 has the title `Protein Secondary Structure Modelling with Probabilistic Networks (Extended Abstract)  
` and the abstract `In this paper we study the performance of probabilistic networks in the context of protein sequence analysis in molecular biology. Specifically, we report the results of our initial experiments applying this framework to the problem of protein secondary structure prediction. One of the main advantages of the probabilistic approach we describe here is our ability to perform detailed experiments where we can experiment with different models. We can easily perform local substitutions (mutations) and measure (probabilistically) their effect on the global structure. Window-based methods do not support such experimentation as readily. Our method is efficient both during training and during prediction, which is important in order to be able to perform many experiments with different networks. We believe that probabilistic methods are comparable to other methods in prediction quality. In addition, the predictions generated by our methods have precise quantitative semantics which is not shared by other classification methods. Specifically, all the causal and statistical independence assumptions are made explicit in our networks thereby allowing biologists to study and experiment with different causal models in a convenient manner. 
`.
Question: Which category should Paper #1130634 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #1130634 has the title `Protein Secondary Structure Modelling with Probabilistic Networks (Extended Abstract)  
` and the abstract `In this paper we study the performance of probabilistic networks in the context of protein sequence analysis in molecular biology. Specifically, we report the results of our initial experiments applying this framework to the problem of protein secondary structure prediction. One of the main advantages of the probabilistic approach we describe here is our ability to perform detailed experiments where we can experiment with different models. We can easily perform local substitutions (mutations) and measure (probabilistically) their effect on the global structure. Window-based methods do not support such experimentation as readily. Our method is efficient both during training and during prediction, which is important in order to be able to perform many experiments with different networks. We believe that probabilistic methods are comparable to other methods in prediction quality. In addition, the predictions generated by our methods have precise quantitative semantics which is not shared by other classification methods. Specifically, all the causal and statistical independence assumptions are made explicit in our networks thereby allowing biologists to study and experiment with different causal models in a convenient manner. 
`.
Question: Which category should Paper #1130634 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