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115
Learning Stochastic Logic Programs
, 2000
"... Stochastic Logic Programs (SLPs) have been shown to be a generalisation of Hidden Markov Models (HMMs), stochastic contextfree grammars, and directed Bayes' nets. A stochastic logic program consists of a set of labelled clauses p:C where p is in the interval [0,1] and C is a firstorder r ..."
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Cited by 1187 (79 self)
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Stochastic Logic Programs (SLPs) have been shown to be a generalisation of Hidden Markov Models (HMMs), stochastic contextfree grammars, and directed Bayes' nets. A stochastic logic program consists of a set of labelled clauses p:C where p is in the interval [0,1] and C is a firstorder rangerestricted definite clause. This paper summarises the syntax, distributional semantics and proof techniques for SLPs and then discusses how a standard Inductive Logic Programming (ILP) system, Progol, has been modied to support learning of SLPs. The resulting system 1) nds an SLP with uniform probability labels on each definition and nearmaximal Bayes posterior probability and then 2) alters the probability labels to further increase the posterior probability. Stage 1) is implemented within CProgol4.5, which differs from previous versions of Progol by allowing userdefined evaluation functions written in Prolog. It is shown that maximising the Bayesian posterior function involves nding SLPs with short derivations of the examples. Search pruning with the Bayesian evaluation function is carried out in the same way as in previous versions of CProgol. The system is demonstrated with worked examples involving the learning of probability distributions over sequences as well as the learning of simple forms of uncertain knowledge.
The KDD Process for Extracting Useful Knowledge from Volumes of Data
 Communications of the ACM
, 1996
"... Knowledge Discovery in Databases creates the context for developing the tools needed to control the flood of data facing organizations that depend on evergrowing databases of business, manufacturing, scientific and personal information. ..."
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Cited by 365 (0 self)
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Knowledge Discovery in Databases creates the context for developing the tools needed to control the flood of data facing organizations that depend on evergrowing databases of business, manufacturing, scientific and personal information.
Correlationbased feature selection for machine learning
, 1998
"... A central problem in machine learning is identifying a representative set of features from which to construct a classification model for a particular task. This thesis addresses the problem of feature selection for machine learning through a correlation based approach. The central hypothesis is that ..."
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Cited by 297 (3 self)
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A central problem in machine learning is identifying a representative set of features from which to construct a classification model for a particular task. This thesis addresses the problem of feature selection for machine learning through a correlation based approach. The central hypothesis is that good feature sets contain features that are highly correlated with the class, yet uncorrelated with each other. A feature evaluation formula, based on ideas from test theory, provides an operational definition of this hypothesis. CFS (Correlation based Feature Selection) is an algorithm that couples this evaluation formula with an appropriate correlation measure and a heuristic search strategy. CFS was evaluated by experiments on artificial and natural datasets. Three machine learning algorithms were used: C4.5 (a decision tree learner), IB1 (an instance based learner), and naive Bayes. Experiments on artificial datasets showed that CFS quickly identifies and screens irrelevant, redundant, and noisy features, and identifies relevant features as long as their relevance does not strongly depend on other features. On natural domains, CFS typically eliminated well over half the features. In most cases, classification accuracy using the reduced feature set equaled or bettered accuracy using the complete feature set.
A Guide to the Literature on Learning Probabilistic Networks From Data
, 1996
"... This literature review discusses different methods under the general rubric of learning Bayesian networks from data, and includes some overlapping work on more general probabilistic networks. Connections are drawn between the statistical, neural network, and uncertainty communities, and between the ..."
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Cited by 203 (0 self)
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This literature review discusses different methods under the general rubric of learning Bayesian networks from data, and includes some overlapping work on more general probabilistic networks. Connections are drawn between the statistical, neural network, and uncertainty communities, and between the different methodological communities, such as Bayesian, description length, and classical statistics. Basic concepts for learning and Bayesian networks are introduced and methods are then reviewed. Methods are discussed for learning parameters of a probabilistic network, for learning the structure, and for learning hidden variables. The presentation avoids formal definitions and theorems, as these are plentiful in the literature, and instead illustrates key concepts with simplified examples. Keywords Bayesian networks, graphical models, hidden variables, learning, learning structure, probabilistic networks, knowledge discovery. I. Introduction Probabilistic networks or probabilistic gra...
Knowledge Discovery and Data Mining: Towards a Unifying Framework
, 1996
"... This paper presents a first step towards a unifying framework for Knowledge Discovery in Databases. We describe links between data mining, knowledge discovery, and other related fields. We then define the KDD process and basic data mining algorithms, discuss application issues and conclude with an a ..."
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Cited by 180 (1 self)
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This paper presents a first step towards a unifying framework for Knowledge Discovery in Databases. We describe links between data mining, knowledge discovery, and other related fields. We then define the KDD process and basic data mining algorithms, discuss application issues and conclude with an analysis of challenges facing practitioners in the field. keywords: Knowledge Discovery in Databases (KDD), Data mining, overview article, large databases, automated analysis, issues and challenges in data mining. To appear: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD96), Portland, Oregon, August 24, 1996, AAAI Press. http://wwwaig. jpl.nasa.gov/kdd96 Knowledge Discovery and Data Mining: Towards a Unifying Framework Usama Fayyad Microsoft Research One Microsoft Way Redmond, WA 98052, USA fayyad@microsoft.com Gregory PiatetskyShapiro GTE Laboratories, MS 44 Waltham, MA 02154, USA gps@gte.com Padhraic Smyth Information and Computer S...
Extracting Comprehensible Models from Trained Neural Networks
, 1996
"... To Mom, Dad, and Susan, for their support and encouragement. ..."
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Cited by 83 (3 self)
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To Mom, Dad, and Susan, for their support and encouragement.
User Modeling in Adaptive Interfaces
 PROCEEDINGS OF THE SEVENTH INTERNATIONAL CONFERENCE ON USER MODELING
, 1999
"... In this paper we examine the notion of adaptive user interfaces, interactive systems that invoke machine learning to improve their interaction with humans. We review some previous work in this emerging area, ranging from software that filters information to systems that support more complex task ..."
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Cited by 72 (6 self)
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In this paper we examine the notion of adaptive user interfaces, interactive systems that invoke machine learning to improve their interaction with humans. We review some previous work in this emerging area, ranging from software that filters information to systems that support more complex tasks like scheduling. After this, we describe three ongoing research efforts that extend this framework in new directions. Finally, we review previous work that has addressed similar issues and consider some challenges that are presented by the design of adaptive user interfaces.
Programming By Demonstration Using Version Space Algebra
, 2001
"... Programming by demonstration enables users to easily personalize their applications, automating repetitive tasks simply by executing a few examples. We formalize programming by demonstration as a machine learning problem: given the changes in the application state that result from the user's de ..."
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Cited by 61 (8 self)
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Programming by demonstration enables users to easily personalize their applications, automating repetitive tasks simply by executing a few examples. We formalize programming by demonstration as a machine learning problem: given the changes in the application state that result from the user's demonstrated actions, learn the general program that maps from one application state to the next. We present a methodology for learning in this space of complex functions. First we extend version spaces to learn arbitrary functions, not just concepts. Then we introduce the version space algebra, a method for composing simpler version spaces to construct more complex spaces. Finally, we apply our version space algebra to the textediting domain and describe an implemented system called SMARTedit that learns repetitive textediting procedures by example. We evaluate our approach by measuring the number of examples required for the system to learn a procedure that works on the remainder of examples, and by an informal user study measuring the e#ort users spend using our system versus performing the task by hand. The results show that SMARTedit is capable of generalizing correctly from as few as one or two examples, and that users generally save a significant amount of e#ort when completing tasks with SMARTedit's help.
Version Space Algebra and its Application to Programming by Demonstration
 In ICML
, 2000
"... Machine learning research has been very successful at producing powerful, broadly applicable classification learners. However, many practical learning problems do not fit the classification framework well, and as a result the initial phase of suitably formulating the problem and incorporating the re ..."
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Cited by 60 (13 self)
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Machine learning research has been very successful at producing powerful, broadly applicable classification learners. However, many practical learning problems do not fit the classification framework well, and as a result the initial phase of suitably formulating the problem and incorporating the relevant domain knowledge can be very difficult and timeconsuming. Here we propose a framework to systematize and speed this process, based on the notion of version space algebra. We extend the notion of version spaces beyond concept learning, and propose that carefullytailored version spaces for complex applications can be built by composing simpler, restricted version spaces. We illustrate our approach with SMARTedit, a programming by demonstration application for repetitive textediting that uses version space algebra to guide a search over textediting action sequences. We demonstrate the system on a suite of repetitive textediting problems and present experimental re...
An Extensible MetaLearning Approach for Scalable and Accurate Inductive Learning
, 1996
"... Much of the research in inductive learning concentrates on problems with relatively small amounts of data. With the coming age of ubiquitous network computing, it is likely that orders of magnitude more data in databases will be available for various learning problems of real world importance. Som ..."
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Cited by 51 (8 self)
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Much of the research in inductive learning concentrates on problems with relatively small amounts of data. With the coming age of ubiquitous network computing, it is likely that orders of magnitude more data in databases will be available for various learning problems of real world importance. Some learning algorithms assume that the entire data set fits into main memory, which is not feasible for massive amounts of data, especially for applications in data mining. One approach to handling a large data set is to partition the data set into subsets, run the learning algorithm on each of the subsets, and combine the results. Moreover, data can be inherently distributed across multiple sites on the network and merging all the data in one location can be expensive or prohibitive. In this thesis we propose, investigate, and evaluate a metalearning approach to integrating the results of mul...