nils-herrmann/clean_scirepeval huggingface dataset:
Learning from Sparse Data by Exploiting Monotonicity Constraints

When training data is sparse, more domain knowledge must be incorporated into the learning algorithm in order to reduce the effective size of the hypothesis space. This paper builds on previous work in which knowledge about qualitative monotonicities was formally represented and incorporated into learning algorithms (e.g., Clark & Matwin's work with the CN2 rule learning algorithm). We show how to interpret knowledge of qualitative influences, and in particular of monotonicities, as constraints on probability distributions, and to incorporate this knowledge into Bayesian network learning algorithms. We show that this yields improved accuracy, particularly with very small training sets (e.g. less than 10 examples).
Transfer Learning with an Ensemble of Background Tasks

We demonstrate the transfer of learning from an ensemble of background tasks, which becomes helpful in cases where a single background task does not transfer well. This approach is accomplished through a simple maximum a posteriori elaboration on the logistic regression approach and tested on real world data. 1 Transfer Learning Via Learned Prior Distributions Transferring knowledge from a famil iar domain or task (call it task A) to an unfamiliar or newly-encountered one (task B) is a fundamental and fascinating aspect of human learning. Although the motivating notion is intuitive, the simple approach of treating the two tasks as identical and pooling their training data does not usually work well. This is presumably because the decision boundaries for A and B are not in exactly the same places in the feature space, even when the feature spaces and input distributions are themselves identical. Hence, more sophisticated methods are required. One interesting approach is to treat task A as defining a form of Bayesian prior distribution for task B. In this paper, we study this approach in a setting where we have many task As and only one task B, and we study whether we can learn a useful prior from those multiple task As that gives effective guidance when learning task B. Consider the well-known logistic regression model,
News and Notes

The News and Notes column of Machine Learning is intended to be a forum for letters to the editor, announcements of conferences, descriptions of research projects, summaries of conferences, and so forth. Contributions from the readership are solicited. Submissions to this column should be sent to Dr. Thomas Dietterich, Department of Computer Science, Oregon State University, Corvallis, OR, 97331 USA (tgd%oregon-state@csnet-relay).
Integrating Learning from Examples into the Search for Diagnostic Policies

This paper studies the problem of learning diagnostic policies from training examples. A diagnostic policy is a complete description of the decision-making actions of a diagnostician (i.e., tests followed by a diagnostic decision) for all possible combinations of test results. An optimal diagnostic policy is one that minimizes the expected total cost, which is the sum of measurement costs and misdiagnosis costs. In most diagnostic settings, there is a tradeoff between these two kinds of costs. 
 
This paper formalizes diagnostic decision making as a Markov Decision Process (MDP). The paper introduces a new family of systematic search algorithms based on the AO* algorithm to solve this MDP. To make AO* efficient, the paper describes an admissible heuristic that enables AO* to prune large parts of the search space. The paper also introduces several greedy algorithms including some improvements over previously-published methods. The paper then addresses the question of learning diagnostic policies from examples. When the probabilities of diseases and test results are computed from training data, there is a great danger of overfitting. To reduce overfitting, regularizers are integrated into the search algorithms. Finally, the paper compares the proposed methods on five benchmark diagnostic data sets. The studies show that in most cases the systematic search methods produce better diagnostic policies than the greedy methods. In addition, the studies show that for training sets of realistic size, the systematic search algorithms are practical on today's desktop computers.
Learning first-order probabilistic models with combining rules

First-order probabilistic models allow us to model situations in which a random variable in the first-order model may have a large and varying numbers of parent variables in the ground ("unrolled") model. One approach to compactly describing such models is to independently specify the probability of a random variable conditioned on each individual parent (or small sets of parents) and then combine these conditional distributions via a combining rule (e.g., Noisy-OR). This paper presents algorithms for learning with combining rules. Specifically, algorithms based on gradient descent and expectation maximization are derived, implemented, and evaluated on synthetic data and on a real-world task. The results demonstrate that the algorithms are able to learn the parameters of both the individual parent-target distributions and the combining rules.
Explanation-Based Learning and Reinforcement Learning: A Unified View

In speedup-learning problems, where full descriptions of operators are known, both explanation-based learning (EBL) and reinforcement learning (RL) methods can be applied. This paper shows that both methods involve fundamentally the same process of propagating information backward from the goal toward the starting state. Most RL methods perform this propagation on a state-by-state basis, while EBL methods compute the weakest preconditions of operators, and hence, perform this propagation on a region-by-region basis. Barto, Bradtke, and Singh (1995) have observed that many algorithms for reinforcement learning can be viewed as asynchronous dynamic programming. Based on this observation, this paper shows how to develop dynamic programming versions of EBL, which we call region-based dynamic programming or Explanation-Based Reinforcement Learning (EBRL). The paper compares batch and online versions of EBRL to batch and online versions of point-based dynamic programming and to standard EBL. The results show that region-based dynamic programming combines the strengths of EBL (fast learning and the ability to scale to large state spaces) with the strengths of reinforcement learning algorithms (learning of optimal policies). Results are shown in chess endgames and in synthetic maze tasks.
Editorial

With this issue, Machine Learning begins publishing an issue every month—12 issues per year. This 50% increase in issues reflects the continuing increase in the number and quality of submissions to the journal. The excitement being generated by machine learning methods can be observed in artificial intelligence meetings worldwide. Points of contact are also being made with several different communities, including the logic programming community, through inductive logic programming; the database community, through knowledge discovery in databases; the uncertain reasoning community, through induction of Bayesian influence diagrams; and the statistical and neural network communities, through our shared interest in fitting complex models to large amounts of data. As always, the journal seeks to provide a forum for the best current research in all areas of machine learning. In this regard, I want to thank Jack Mostow for his service as an Action Editor for the past three years. As a very conscientious editor, Jack worked hard to produce excellent articles for the journal. I am pleased to note that Ross Quinlan has again taken on the responsibilities of an Action Editor for the journal. We also welcome two new members of the editorial board, Alberto Segre (who is also  and Jeff Schlimmer. Finally, I want to thank the Executive Editor of the past four years, Jaime Carbonell, for his wonderful efforts to make the journal a high quality publication with a broad scope. He recruited editors for several special issues on a wide range of topics, and nurtured the issues through the difficult review process. Several of these special issues have already appeared, but several more will appear in the coming months.
Two heuristics for solving POMDPs having a delayed need to observe

A common heuristic for solving Partial ly Observable Markov Decision Problems POMDPs is to rst solve the underlying Markov Decision Process MDP and then con struct a POMDP policy by performing a xed depth lookahead search in the POMDP and evaluating the leaf nodes using the MDP value function A problem with this approximation is that it does not account for the need to choose actions in order to gain information about the state of the world particularly when those ob servation actions are needed at some point in the future This paper proposes two heuristics that are better than the MDP approximation in POMDPs where there is a delayed need to observe The rst approximation introduced in is the even odd POMDP in which the world is assumed to be fully observable every other time step The even odd POMDP can be converted into an equivalent MDP the even MDP whose value function captures some of the sensing costs of the original POMDP An online policy consisting in a step lookahead search com bined with the value function of the even MDP gives an approximation to the POMDP s value function that is at least as good as the method based on the value function of the underlying MDP The second POMDP approximation is applicable to a special kind of POMDP which we call the Cost Observable Markov Decision Problem COMDP In a COMDP the actions are partitioned into those that change the state of the world and those that are pure observa tion actions For such problems we describe the chain MDP algorithm which in many cases is able to capture more of the sensing costs than the even odd POMDP approximation We prove that both heuristics compute value functions that are upper bounded by i e bet ter than the value function of the underlying MDP and in the case of the even MDP also lower bounded by the POMDP s optimal value function We show cases where the chain MDP online policy is better equal or worse than the even MDP online policy
Learning at the knowledge level

When Newell introduced the concept of the knowledge level as a useful level of description for computer systems, he focused on the representation of knowledge. This paper applies the knowledge level notion to the problem of knowledge acquisition. Two interesting issues arise. First, some existing machine learning programs appear to be completely static when viewed at the knowledge level. These programs improve their performance without changing their ‘knowledge’. Second, the behavior of some other machine learning programs cannot be predicted or described at the knowledge level. These programs take unjustified inductive leaps. The first programs are called symbol level learning (SLL) programs; the second, nondeductive knowledge level learning (NKLL) programs. The paper analyzes both of these classes of learning programs and speculates on the possibility of developing coherent theories of each. A theory of symbol level learning is sketched, and some reasons are presented for believing that a theory of NKLL will be difficult to obtain.
Improving SVM accuracy by training on auxiliary data sources

The standard model of supervised learning assumes that training and test data are drawn from the same underlying distribution. This paper explores an application in which a second, auxiliary, source of data is available drawn from a different distribution. This auxiliary data is more plentiful, but of significantly lower quality, than the training and test data. In the SVM framework, a training example has two roles: (a) as a data point to constrain the learning process and (b) as a candidate support vector that can form part of the definition of the classifier. The paper considers using the auxiliary data in either (or both) of these roles. This auxiliary data framework is applied to a problem of classifying images of leaves of maple and oak trees using a kernel derived from the shapes of the leaves. Experiments show that when the training data set is very small, training with auxiliary data can produce large improvements in accuracy, even when the auxiliary data is significantly different from the training (and test) data. The paper also introduces techniques for adjusting the kernel scores of the auxiliary data points to make them more comparable to the training data points.
An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization

Bagging and boosting are methods that generate a diverse ensemble of classifiers by manipulating the training data given to a “base” learning algorithm. Breiman has pointed out that they rely for their effectiveness on the instability of the base learning algorithm. An alternative approach to generating an ensemble is to randomize the internal decisions made by the base algorithm. This general approach has been studied previously by Ali and Pazzani and by Dietterich and Kong. This paper compares the effectiveness of randomization, bagging, and boosting for improving the performance of the decision-tree algorithm C4.5. The experiments show that in situations with little or no classification noise, randomization is competitive with (and perhaps slightly superior to) bagging but not as accurate as boosting. In situations with substantial classification noise, bagging is much better than boosting, and sometimes better than randomization.
Editorial

Beginning with Volume 9, it will be my privilege to serve as Editor-in-Chief of Machine Learning. At present, Jaime Carbonell and I are occupying this position together to make a smooth transition before he steps down. I want to thank both of my predecessors: Pat Langley, who so ably launched the journal anil set high standards for quality, and Jaime Carbonell, who has presided over the rapid growth of the journal to the point where we are now publishing eight issues per year. I have four goals for Machine Learning over the coming years. First, I want to maintain and strengthen the quality of the research reported here. Machine Learning will continue to seek papers that make a substantial, novel contribution to our knowledge of learning in machines. Clarity of writing will remain an important criterion as well. Second, I want to shorten the total time required to move from the initial submitted manuscript to a published paper. This requires more aggressive tracking of manuscripts thrgugh the review process. It also means that manuscripts will be expected to be wellpolished at the time they are submitted. Authors should not rely on the comments of the reviewers to help them write their papers. Third, I am already working with our publisher to improve the production process so thai: we can accept manuscripts for final submission in electronic form (most likely in LaTeX or TeX format). This should reduce errors introduced when the manuscripts are typeset. It should also help reduce the time-to-publication by reducing the need for proofreading typeset material. Finally, I hope to continue broadening the scope of Machine Learning to form bridges witch other disciplines that study learning methods, such as artificial neural networks,
Training conditional random fields via gradient tree boosting

Conditional Random Fields (CRFs; Lafferty, McCallum, & Pereira, 2001) provide a flexible and powerful model for learning to assign labels to elements of sequences in such applications as part-of-speech tagging, text-to-speech mapping, protein and DNA sequence analysis, and information extraction from web pages. However, existing learning algorithms are slow, particularly in problems with large numbers of potential input features. This paper describes a new method for training CRFs by applying Friedman's (1999) gradient tree boosting method. In tree boosting, the CRF potential functions are represented as weighted sums of regression trees. Regression trees are learned by stage-wise optimizations similar to Adaboost, but with the objective of maximizing the conditional likelihood P(Y|X) of the CRF model. By growing regression trees, interactions among features are introduced only as needed, so although the parameter space is potentially immense, the search algorithm does not explicitly consider the large space. As a result, gradient tree boosting scales linearly in the order of the Markov model and in the order of the feature interactions, rather than exponentially like previous algorithms based on iterative scaling and gradient descent.
A Comparison of ID3 and Backpropagation for English Text-To-Speech Mapping

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 closely matched. More complex statistical procedures can improve the performance of both BP and ID3 substantially in this domain.
Bias-Variance Analysis of Support Vector Machines for the Development of SVM-Based Ensemble Methods

Bias-variance analysis provides a tool to study learning algorithms and can be used to properly design ensemble methods well tuned to the properties of a specific base learner. Indeed the effectiveness of ensemble methods critically depends on accuracy, diversity and learning characteristics of base learners. We present an extended experimental analysis of bias-variance decomposition of the error in Support Vector Machines (SVMs), considering Gaussian, polynomial and dot product kernels. A characterization of the error decomposition is provided, by means of the analysis of the relationships between bias, variance, kernel type and its parameters, offering insights into the way SVMs learn. The results show that the expected trade-off between bias and variance is sometimes observed, but more complex relationships can be detected, especially in Gaussian and polynomial kernels. We show that the bias-variance decomposition offers a rationale to develop ensemble methods using SVMs as base learners, and we outline two directions for developing SVM ensembles, exploiting the SVM bias characteristics and the bias-variance dependence on the kernel param
An Experimental Comparison of the Nearest-Neighbor and Nearest-Hyperrectangle Algorithms

Algorithms based on Nested Generalized Exemplar (NGE) theory (Salzberg, 1991) classify new data points by computing their distance to the nearest “generalized exemplar” (i.e., either a point or an axis-parallel rectangle). They combine the distance-based character of nearest neighbor (NN) classifiers with the axis-parallel rectangle representation employed in many rule-learning systems. An implementation of NGE was compared to the k-nearest neighbor (kNN) algorithm in 11 domains and found to be significantly inferior to kNN in 9 of them. Several modifications of NGE were studied to understand the cause of its poor performance. These show that its performance can be substantially improved by preventing NGE from creating overlapping rectangles, while still allowing complete nesting of rectangles. Performance can be further improved by modifying the distance metric to allow weights on each of the features (Salzberg, 1991). Best results were obtained in this study when the weights were computed using mutual information between the features and the output class. The best version of NGE developed is a batch algorithm (BNGE FWMI) that has no user-tunable parameters. 's performance is comparable to the first-nearest neighbor algorithm (also incorporating feature weights). However, the k-nearest neighbor algorithm is still significantly superior to BNGE FWMI in 7 of the 11 domains, and inferior to it in only 2. We conclude that, even with our improvements, the NGE approach is very sensitive to the shape of the decision boundaries in classification problems. In domains where the decision boundaries are axis-parallel, the NGE approach can produce excellent generalization with interpretable hypotheses. In all domains tested, NGE algorithms require much less memory to store generalized exemplars than is required by NN algorithms.
An experimental comparison of the nearest-neighbor and nearest-hyperrectangle algorithms

Algorithms based on Nested Generalized Exemplar (NGE) theory (Salzberg, 1991) classify new data points by computing their distance to the nearest “generalized exemplar” (i.e., either a point or an axis-parallel rectangle). They combine the distance-based character of nearest neighbor (NN) classifiers with the axis-parallel rectangle representation employed in many rule-learning systems. An implementation of NGE was compared to thek-nearest neighbor (kNN) algorithm in 11 domains and found to be significantly inferior to kNN in 9 of them. Several modifications of NGE were studied to understand the cause of its poor performance. These show that its performance can be substantially improved by preventing NGE from creating overlapping rectangles, while still allowing complete nesting of rectangles. Performance can be further improved by modifying the distance metric to allow weights on each of the features (Salzberg, 1991). Best results were obtained in this study when the weights were computed using mutual information between the features and the output class. The best version of NGE developed is a batch algorithm (BNGE FWMI) that has no user-tunable parameters. 's performance is comparable to the first-nearest neighbor algorithm (also incorporating feature weights). However, thek-nearest neighbor algorithm is still significantly superior to BNGE FWMI in 7 of the 11 domains, and inferior to it in only 2. We conclude that, even with our improvements, the NGE approach is very sensitive to the shape of the decision boundaries in classification problems. In domains where the decision boundaries are axis-parallel, the NGE approach can produce excellent generalization with interpretable hypotheses. In all domains tested, NGE algorithms require much less memory to store generalized exemplars than is required by NN algorithms.
A comparison of ID3 and backpropagation for English text-to-speech mapping

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 closely matched. More complex statistical procedures can improve the performance of both BP and ID3 substantially in this domain.