Jude Shavlik

AAAI Conference 2014 Conference Paper

Relational One-Class Classification: A Non-Parametric Approach

• Tushar Khot
• Sriraam Natarajan
• Jude Shavlik

One-class classification approaches have been proposed in the literature to learn classifiers from examples of only one class. But these approaches are not directly applicable to relational domains due to their reliance on a feature vector or a distance measure. We propose a nonparametric relational one-class classification approach based on first-order trees. We learn a tree-based distance measure that iteratively introduces new relational features to differentiate relational examples. We update the distance measure so as to maximize the one-class classification performance of our model. We also relate our model definition to existing work on probabilistic combination functions and density estimation. We experimentally show that our approach can discover relevant features and outperform three baseline approaches.

NeurIPS Conference 2011 Conference Paper

Advice Refinement in Knowledge-Based SVMs

• Gautam Kunapuli
• Richard Maclin
• Jude Shavlik

Knowledge-based support vector machines (KBSVMs) incorporate advice from domain experts, which can improve generalization significantly. A major limitation that has not been fully addressed occurs when the expert advice is imperfect, which can lead to poorer models. We propose a model that extends KBSVMs and is able to not only learn from data and advice, but also simultaneously improve the advice. The proposed approach is particularly effective for knowledge discovery in domains with few labeled examples. The proposed model contains bilinear constraints, and is solved using two iterative approaches: successive linear programming and a constrained concave-convex approach. Experimental results demonstrate that these algorithms yield useful refinements to expert advice, as well as improve the performance of the learning algorithm overall.

IJCAI Conference 2011 Conference Paper

Imitation Learning in Relational Domains: A Functional-Gradient Boosting Approach

• Sriraam Natarajan
• Saket Joshi
• Prasad Tadepalli
• Kristian Kersting
• Jude Shavlik

Imitation learning refers to the problem of learning how to behave by observinga teacher in action. We consider imitation learning in relational domains, in which there is a varying number of objects and relations among them. In prior work, simple relational policies are learned by viewing imitation learning as supervised learning of a function from states to actions. For propositional worlds, functional gradient methods have been proved to be beneficial. They are simpler to implement than most existing methods, more efficient, more naturally satisfy common constraints on the cost function, and better represent our prior beliefs about the form of the function. Building on recent generalizations of functional gradient boosting to relational representations, we implement a functional gradient boosting approach to imitation learning in relational domains. In particular, given a set of traces from the human teacher, our system learns a policy in the form of a set of relational regression trees that additively approximate the functional gradients. The use of multiple additive trees combined with relational representation allows for learning more expressive policies than what has been done before. We demonstrate the usefulness of our approach in several different domains.

IJCAI Conference 2009 Conference Paper

• Jude Shavlik
• Sriraam Natarajan

Statistical-relational reasoning has received much attention due to its ability to robustly model complex relationships. A key challenge is tractable inference, especially in domains involving many objects, due to the combinatorics involved. One can accelerate inference by using approximation techniques, “lazy” algorithms, etc. We consider Markov Logic Networks (MLNs), which involve counting how often logical formulae are satisfied. We propose a preprocessing algorithm that can substantially reduce the effective size of MLNs by rapidly counting how often the evidence satis- fies each formula, regardless of the truth values of the query literals. This is a general preprocessing method that loses no information and can be used for any MLN inference algorithm. We evaluate our algorithm empirically in three real-world domains, greatly reducing the work needed during subsequent inference. Such reduction might even allow exact inference to be performed when sampling methods would be otherwise necessary.

AAAI Conference 2007 Conference Paper

Refining Rules Incorporated into Knowledge-Based Support Vector Learners Via Successive Linear Programming

• Richard Maclin
• Jude Shavlik

Knowledge-based classification and regression methods are especially powerful forms of learning. They allow a system to take advantage of prior domain knowledge supplied either by a human user or another algorithm, combining that knowledge with data to produce accurate models. A limitation of the use of prior knowledge occurs when the provided knowledge is incorrect. Such knowledge likely still contains useful information, but knowledge-based learners might not be able to fully exploit such information. In fact, incorrect knowledge can lead to poorer models than result from knowledge-free learners. We present a support-vector method for incorporating and refining domain knowledge that not only allows the learner to make use of that knowledge, but also suggests changes to the provided knowledge. Our approach is built on the knowledge-based classification and regression methods presented by Fung, Mangasarian, & Shavlik (2002; 2003) and by Mangasarian, Shavlik, & Wild (2004). Experiments on artificial data sets with known properties, as well as on a real-world data set, demonstrate that our method learns more accurate models while also adjusting the provided rules in intuitive ways. Our new algorithm provides an appealing extension to knowledge-based, support-vector learning that is not only able to combine knowledge from rules with data, but is also able to use the data to modify and change those rules to better fit the data.

NeurIPS Conference 2004 Conference Paper

Pictorial Structures for Molecular Modeling: Interpreting Density Maps

• Frank Dimaio
• George Phillips
• Jude Shavlik

X-ray crystallography is currently the most common way protein structures are elucidated. One of the most time-consuming steps in the crystallographic process is interpretation of the electron density map, a task that involves finding patterns in a three-dimensional picture of a protein. This paper describes DEFT (DEFormable Template), an algorithm using pictorial structures to build a flexible protein model from the protein's amino-acid sequence. Matching this pictorial structure into the density map is a way of automating density-map interpretation. Also described are several extensions to the pictorial structure matching algorithm necessary for this automated interpretation. DEFT is tested on a set of density maps ranging from 2 to 4Å resolution, producing root- mean-squared errors ranging from 1. 38 to 1. 84Å.

NeurIPS Conference 2002 Conference Paper

Knowledge-Based Support Vector Machine Classifiers

• Glenn Fung
• Olvi Mangasarian
• Jude Shavlik

Prior knowledge in the form of multiple polyhedral sets, each be(cid: 173) longing to one of two categories, is introduced into a reformulation of a linear support vector machine classifier. The resulting formu(cid: 173) lation leads to a linear program that can be solved efficiently. Real world examples, from DNA sequencing and breast cancer prognosis, demonstrate the effectiveness of the proposed method. Numerical results show improvement in test set accuracy after the incorpo(cid: 173) ration of prior knowledge into ordinary, data-based linear support vector machine classifiers. One experiment also shows that a lin(cid: 173) ear classifier, based solely on prior knowledge, far outperforms the direct application of prior knowledge rules to classify data. Keywords: use and refinement of prior knowledge, sup(cid: 173) port vector machines, linear programming

NeurIPS Conference 1995 Conference Paper

Extracting Tree-Structured Representations of Trained Networks

• Mark Craven
• Jude Shavlik

A significant limitation of neural networks is that the represen(cid: 173) tations they learn are usually incomprehensible to humans. We present a novel algorithm, TREPAN, for extracting comprehensible, symbolic representations from trained neural networks. Our algo(cid: 173) rithm uses queries to induce a decision tree that approximates the concept represented by a given network. Our experiments demon(cid: 173) strate that TREPAN is able to produce decision trees that maintain a high level of fidelity to their respective networks while being com(cid: 173) prehensible and accurate. Unlike previous work in this area, our algorithm is general in its applicability and scales well to large net(cid: 173) works and problems with high-dimensional input spaces.

NeurIPS Conference 1995 Conference Paper

Generating Accurate and Diverse Members of a Neural-Network Ensemble

• David Opitz
• Jude Shavlik

Neural-network ensembles have been shown to be very accurate classification techniques. Previous work has shown that an effec(cid: 173) tive ensemble should consist of networks that are not only highly correct, but ones that make their errors on different parts of the input space as well. Most existing techniques, however, only in(cid: 173) directly address the problem of creating such a set of networks. In this paper we present a technique called ADDEMUP that uses genetic algorithms to directly search for an accurate and diverse set of trained networks. ADDEMUP works by first creating an ini(cid: 173) tial population, then uses genetic operators to continually create new networks, keeping the set of networks that are as accurate as possible while disagreeing with each other as much as possible. Ex(cid: 173) periments on three DNA problems show that ADDEMUP is able to generate a set of trained networks that is more accurate than sev(cid: 173) eral existing approaches. Experiments also show that ADDEMUP is able to effectively incorporate prior knowledge, if available, to improve the quality of its ensemble.

NeurIPS Conference 1995 Conference Paper

Rapid Quality Estimation of Neural Network Input Representations

• Kevin Cherkauer
• Jude Shavlik

The choice of an input representation for a neural network can have a profound impact on its accuracy in classifying novel instances. However, neural networks are typically computationally expensive to train, making it difficult to test large numbers of alternative representations. This paper introduces fast quality measures for neural network representations, allowing one to quickly and ac(cid: 173) curately estimate which of a collection of possible representations for a problem is the best. We show that our measures for ranking representations are more accurate than a previously published mea(cid: 173) sure, based on experiments with three difficult, real-world pattern recognition problems.

NeurIPS Conference 1991 Conference Paper

Interpretation of Artificial Neural Networks: Mapping Knowledge-Based Neural Networks into Rules

• Geoffrey Towell
• Jude Shavlik

We propose and empirically evaluate a method for the extraction of expert(cid: 173) comprehensible rules from trained neural networks. Our method operates in the context of a three-step process for learning that uses rule-based domain knowledge in combination with neural networks. Empirical tests using real(cid: 173) worlds problems from molecular biology show that the rules our method extracts from trained neural networks: closely reproduce the accuracy of the network from which they came, are superior to the rules derived by a learning system that directly refines symbolic rules, and are expert-comprehensible.

NeurIPS Conference 1991 Conference Paper

Refining PID Controllers using Neural Networks

• Gary Scott
• Jude Shavlik
• W. Ray

The KBANN approach uses neural networks to refine knowledge that can be written in the form of simple propositional rules. We extend this idea further by presenting the MANNCON algorithm by which the mathematical equations governing a PID controller determine the topology and initial weights of a network, which is further trained using backpropagation. We apply this method to the task of controlling the outflow and temperature of a water tank, producing statistically-significant gains in accuracy over both a standard neural network approach and a non-learning PID con(cid: 173) troller. Furthermore, using the PID knowledge to initialize the weights of the network produces statistically less variation in testset accuracy when compared to networks initialized with small random numbers.

NeurIPS Conference 1990 Conference Paper

Training Knowledge-Based Neural Networks to Recognize Genes in DNA Sequences

• Michiel Noordewier
• Geoffrey Towell
• Jude Shavlik

We describe the application of a hybrid symbolic/connectionist machine learning algorithm to the task of recognizing important genetic sequences. The symbolic portion of the KBANN system utilizes inference rules that provide a roughly-correct method for recognizing a class of DNA sequences known as eukaryotic splice-junctions. We then map this "domain theory" into a neural network and provide training examples. Using the samples, the neural network's learning algorithm adjusts the domain theory so that it properly classifies these DNA sequences. Our procedure constitutes a general method for incorporating preexisting knowledge into artificial neural networks. We present an experiment in molecular genetics that demonstrates the value of doing so.

IJCAI Conference 1989 Conference Paper

An Experimental Comparison of Symbolic and

• Raymond Mooney
• Jude Shavlik
• Geoffrey Towel I
• Alan Gove