Publications

• Kirlin, P.B., & Utgoff, P.E. (2008). A Framework for Automated Schenkerian Analysis. In Proceedings of the Ninth International Conference on Music Information Retrieval (pp. 363--368). Philadelphia. (.pdf)
• Utgoff, P.E., & Kirlin, P.B. (2006). Detecting Motives and Recurring Patterns in Polyphonic Music. In Proceedings of the International Computer Music Conference (pp. 487-494). New Orleans. (.ps) (.pdf)
• Kirlin, P.B., & Utgoff, P.E. (2005). VoiSe: Learning to segregate voices in explicit and implicit polyphony. In Reiss, J. D., & Wiggins, G. A. (Eds.), Proceedings of the Sixth International Conference on Music Information Retrieval (pp. 552-557). London: Queen Mary, University of London. (letter .ps.gz) (letter .pdf) (a4 .ps.gz) (a4 .pdf)
• Stracuzzi, D.J. (2005). Scalable knowledge acquisition through memory organization. International and Interdisciplinary Conference on Knowledge Representation and Reasoning (AKRR 05). 57-64. Helsinki, Finland: Helsinki University of Technology. (.ps) (.pdf) Best Student Paper
• Blaschko, M.B., Holness, G., Mattar, M.A., Lisin, D., Utgoff, P.E., Hanson, A.R., Schultz, H.J., & Riseman, E.M. (2005). Automatic in situ identification of plankton. Workshop on Applications of Computer Vision (pp. 79-86).
• Stracuzzi, D.J., & Utgoff, P.E. (2004). Randomized variable elimination. Journal of Machine Learning Research, 5, 1331-1362. (.ps) (.pdf)
• Stoddard, J., Raphael, C., & Utgoff, P.E. (2004). Well-tempered spelling: A key-invariant pitch spelling algorithm. International Symposium on Music Information Retrieval.
• Precup, D., & Utgoff, P.E. (2004). Classification using Phi-machines and constructive function approximation. Machine Learning, 55, 31-52.
• Utgoff, P.E., & Stracuzzi, D.J. (2002). Many-layered learning. Neural Computation, 14, 2497-2529. (.ps), (.pdf)
• Stracuzzi, D.J., & Utgoff, P.E. (2002). Randomized variable elimination. Proceedings of the Nineteenth International Conference on Machine Learning (pp. 594-601). Sydney, Australia: Morgan Kaufmann. (.ps) (.pdf)
• Utgoff, P.E., & Stracuzzi, D.J. (2002). Many-layered learning. Proceedings of the Second International Conference on Development and Learning (pp. 141-146). (.ps) (.pdf)
• Utgoff, P.E., & Cochran, R.P. (2001). A least-certainty heuristic for selective search. Proceedings of the Second International Conference on Computers and Games (pp. 1-18). Springer Verlag. (.ps), (.pdf)
• Utgoff, P.E. (2001). Feature construction for game playing (pp. 131-152). In Fuerenkranz & Kubat (Eds.), Machines that learn to play games. Nova Science Publishers. (.ps), (.pdf)
• Piater, J.H., Riseman, E.M., & Utgoff, P.E. (1999). Interactively training pixel classifiers. International Journal of Pattern Recognition and Artificial Intelligence, 13, 171-193.
• Utgoff, P.E., & Stracuzzi, D.J. (1999). Approximation via value unification. Proceedings of the Sixteenth International Conference on Machine Learning (pp. 425-432). Ljubljana: Morgan Kaufmann. (.ps), (.pdf)
• Utgoff, P.E. (1998). Decision trees (pp. 222-224). In Wilson & Keil (Eds.), The MIT encyclopedia of cognitive sciences. Bradford. (.ps), (.pdf)
• Utgoff, P.E., & Cohen, P.R. (1998). Applicability of reinforcement learning. Proceedings of the 1998 ICML Workshop on the Methodology of Applying Machine Learning (pp. 37-43). AAAI Press Report WS-98-16.
• Utgoff, P.E., & Precup, D. (1998). Constructive function approximation (pp. 219-235). In Liu & Motoda (Eds.), Feature extraction, construction, and selection: A data-mining perspective. Kluwer. (.ps), (.pdf)
• Moss, J.E.B., Utgoff, P.E., Cavazos, J., Precup, D., Stefanovic, D., Brodley, C., & Scheeff, D. (1998). Learning to schedule straight-line code. Advances in Neural Information Processing Systems (pp. 929-935). San Mateo, CA: Morgan Kaufmann.
• Piater, J., Riseman, E., & Utgoff, P.E. (1998). Interactively training pixel classifiers. Eleventh International FLAIRS Conference (FLAIRS-98) (pp. 57-61).
• Precup, D., & Utgoff, P.E. (1998). Classification using phi-machines and constructive function approximation. Proceedings of the Fifteenth International Conference on Machine Learning (pp. 439-444).
• Schmill, M.D., Rosenstein, M.T., Cohen, P.R., & Utgoff, P.E. (1998). Learning what is relevant to the effects of actions for a mobile robot. Proceedings of the Second International Conference on Autonomous Agents (pp. 247-253).
• Utgoff, P.E., Berkman, N.C., & Clouse, J.A. (1997). Decision tree induction based on efficient tree restructuring. Machine Learning, 29, 5-44. (.ps), (.pdf)
• Clouse, J.A. (1996). The role of training in reinforcement learning. In Donahoe (Ed.), Neural Network Models of Cognition: Biobehavioral Foundations. Amsterdam: Elsevier Science Publishers.
• Brodley, C.E., & Utgoff, P.E. (1995). Multivariate decision trees. Machine Learning, 19, 45-77.
• Brodley, C.E. (1995). Recursive automatic bias selection for classifier construction. Machine Learning, 20, 63-94.
• Clouse, J.A. (1995). Learning from an automated training agent. Proceedings: ML95 Workshop on `Agents that Learn from Other Agents'. (\verb@http://www.cs.wisc.edu/shavlik/ml95w1/@.)
• Brodley, C.E., & Utgoff, P.E. (1994). Dynamic recursive model class selection for classifier construction. In Cheeseman & Oldford (Eds.), Selecting Models from Data: Artificial Intelligence and Statistics IV. New York: Springer-Verlag.
• Draper, B.A., Brodley, C.E., & Utgoff, P.E. (1994). Goal-directed classification using linear machine decision trees. IEEE Transactions on Pattern Analysis and Machine Intelligence, 16, 888-893. (Special Issue on Vision and Machine Learning)
• Utgoff, P.E. (1994). An improved algorithm for incremental induction of decision trees. Machine Learning: Proceedings of the Eleventh International Conference (pp. 318-325). New Brunswick, NJ: Morgan Kaufmann.
• Brodley, C.E., & Utgoff, P.E. (1993). Dynamic recursive model class selection for classifier construction. Preliminary Papers of the Fourth International Workshop on Artificial Intelligence and Statistics (pp. 179-184).
• Brodley, C.E. (1993). Addressing the selective superiority problem: Automatic algorithm/model class selection. Machine Learning: Proceedings of the Tenth International Conference (pp. 17-24). Amherst, MA: Morgan Kaufmann.
• Fawcett, T.E., & Utgoff, P.E. (1992). Automatic feature generation for problem solving systems. Machine Learning: Proceedings of the Ninth International Conference (pp. 144-153). San Mateo, CA: Morgan Kaufmann.
• Clouse, J.A., & Utgoff, P.E. (1992). A teaching method for reinforcement learning. Machine Learning: Proceedings of the Ninth International Conference (pp. 92-101). San Mateo, CA: Morgan Kaufmann.
• Callan, J.P., & Utgoff, P.E. (1991). Constructive induction on domain knowledge. Proceedings of the Ninth National Conference on Artificial Intelligence (pp. 614-619). Anaheim, CA: MIT Press.
• Callan, J.P., & Utgoff, P.E. (1991). A transformational approach to constructive induction. Machine Learning: Proceedings of the Eighth International Workshop (pp. 122-126). Evanston, IL: Morgan Kaufmann.
• Callan, J.P., Fawcett, T.E., & Rissland, E.L. (1991). CABOT: An adaptive approach to case-based search. Proceedings of the Twelfth International Joint Conference on Artificial Intelligence (pp. 803-808). Sidney, Australia: Morgan Kaufmann.
• Callan, J.P., Fawcett, T.E., & Rissland, E.L. (1991). Adaptive case-based reasoning. Proceedings of the DARPA Workshop on Case-Based Reasoning (pp. 179-190). Washington, D.C.: Morgan Kaufmann.
• Fawcett, T.E., & Utgoff, P.E. (1991). A hybrid method for feature generation. Machine Learning: Proceedings of the Eighth International Workshop (pp. 137-141). Evanston, IL: Morgan Kaufmann.
• Saxena, S. (1991). On the effect of instance representation on generalization. Machine Learning: Proceedings of the Eighth International Workshop. Evanston, IL: Morgan Kaufmann.
• Utgoff, P.E., & Clouse, J.A. (1991). Two kinds of training information for evaluation function learning. Proceedings of the Ninth National Conference on Artificial Intelligence (pp. 596-600). Anaheim, CA: MIT Press. (.ps), (.pdf)
• Saxena, S. (1990). Using description length to evaluate input representations for learning. Proceedings of the AAAI Spring Symposium on the Theory and Application of Minimal Length Encoding (pp. 135-139).
• Utgoff, P.E., & Brodley, C.E. (1990). An incremental method for finding multivariate splits for decision trees. Proceedings of the Seventh International Conference on Machine Learning (pp. 58-65). Austin, TX: Morgan Kaufmann. (.ps), (.pdf)
• Yee, R.C., Saxena, S., Utgoff, P.E., & Barto, A.G. (1990). Explaining temporal-differences to create useful concepts for evaluating states. Proceedings of the Eighth National Conference on Artificial Intelligence. Boston, MA: Morgan Kaufmann.
• Callan, J.P. (1989). Knowledge-based feature generation. Proceedings of the Sixth International Workshop on Machine Learning (pp. 441-443). Ithaca, NY: Morgan Kaufmann.
• Fawcett, T. (1989). Learning from plausible explanations. Proceedings of the Sixth International Workshop on Machine Learning (pp. 37-39). Ithaca, NY: Morgan Kaufmann.
• Saxena, S. (1989). Evaluating alternative instance representations. Proceedings of the Sixth International Workshop on Machine Learning (pp. 465-468). Ithaca, NY: Morgan Kaufmann.
• Utgoff, P.E. (1989). Improved training via incremental learning. Proceedings of the Sixth International Workshop on Machine Learning. Ithaca, NY: Morgan Kaufmann.
• Utgoff, P.E. (1989). Incremental induction of decision trees. Machine Learning, 4, 161-186. (.ps), (.pdf)
• Utgoff, P.E. (1989). Perceptron trees: A case study in hybrid concept representations. Connection Science, 1, 377-391.
• Utgoff, P.E. (1988). ID5: An incremental ID3. Proceedings of the Fifth International Conference on Machine Learning (pp. 107-120). Ann Arbor, MI: Morgan Kaufman.
• Utgoff, P.E. (1988). Perceptron trees: A case study in hybrid concept representations. Proceedings of the Seventh National Conference on Artificial Intelligence (pp. 601-606). Saint Paul, MN: Morgan Kaufmann.
• Utgoff, P.E., & Heitman, P.S. (1988). Learning and generalizing move selection preferences. Proceedings of the AAAI Symposium on Computer Game Playing (pp. 36-40). Palo Alto, CA.
• Utgoff, P.E., & Saxena, S. (1988). Obtaining efficient classifiers from explanations. Proceedings of the AAAI Symposium on Explanation Based Learning (pp. 47-51). Palo Alto, CA.
• Connell, Margaret E., & Utgoff, Paul E. (1987). Learning to control a dynamical system. Proceedings of the Sixth National Conference on Artificial Intelligence (pp. 456-460). Seattle, WA: Morgan Kaufmann.
• Connell, Margaret, E., & Utgoff, Paul E. (1987). Learning to control a dynamical physical system. Computational Intelligence, 3, 330-337.
• Utgoff, P.E., & Saxena, S. (1987). Learning a preference predicate. Proceedings of the Fourth International Workshop on Machine Learning (pp. 115-121). Irvine, CA: Morgan Kaufmann.
• Utgoff, P.E. (1986). Machine learning of inductive bias. Hingham, MA: Kluwer. (reviewed in IEEE Expert, Fall 1986)

Unpublished Reports

• Utgoff, P.E., Raphael, C., & Stoddard, J. (2004). Detecting motives and recurring patterns in polyphonic music, (Technical Report 04-31), Amherst, MA: University of Massachusetts, Computer Science Department.
• Utgoff, P.E., Ding, G., & Riseman, E.R. (2003). Feature sets for texture classification, (03-38), Amherst, MA: University of Massachusetts, Computer Science Department.
• Utgoff, P.E., Jensen, D., & Lesser, V. (2000). Inferring task structure from data, (Technical Report TR-00-54), Amherst, MA: University of Massachusetts, Computer Science.
• Stracuzzi, D.J., & Utgoff, P.E. (2000). Feature compilation, (TR-00-18), Amherst, MA: University of Massachusetts, Computer Science Department.
• Utgoff, P.E., & Qian, J. (1999). A new polynomial function approximation algorithm, (Technical Report TR-99-20), Amherst, MA: University of Massachusetts, Computer Science.
• Utgoff, P.E., & Precup, D. (1997). Relative value function approximation, (Technical Report 97-03), Amherst, MA: University of Massachusetts, Department of Computer Science.
• Utgoff, P.E., & Precup, D. (1997). Constructive function approximation, (Technical Report 97-04), Amherst, MA: University of Massachusetts, Department of Computer Science.
• Clouse, J.A. (1997). On integrating apprentice learning and reinforcement learning, Doctoral Dissertation (Technical Report 97-26), Amherst, MA: University of Massachusetts, Department of Computer Science.
• Utgoff, P.E., & Clouse, J.A. (1996). A Kolmogorov-Smirnoff metric for decision tree induction, (Technical Report 96-3), Amherst, MA: University of Massachusetts, Department of Computer Science.
• Utgoff, P.E. (1996). ELF: An evaluation function learner that constructs its own features, (Technical Report 96-65), Amherst, MA: University of Massachusetts, Department of Computer Science.
• Clouse, J.A. (1996). An introspection approach to querying a trainer, (Technical Report 96-13), Amherst, MA: University of Massachusetts, Department of Computer Science.
• Utgoff, P.E. (1995). Internet program competition, (Technical Report 95-67), Amherst, MA: University of Massachusetts, Department of Computer Science.
• Clouse, J.A. (1995). On training automated agents, (Technical Report 95-109), Amherst, MA: University of Massachusetts, Computer Science Department.
• Clouse, J.A. (1995). Action set approach to reinforcement learning, (Technical Report 95-108), Amherst, MA: University of Massachusetts, Computer Science Department.
• Berkman, N.C. (1995). Value grouping for binary decision trees, (Technical Report 95-19), Amherst, MA: University of Massachusetts, Department of Computer Science.
• Berkman, N.C., & Sandholm, T.W. (1995). What should be minimized in a decision tree: A re-examination, (Technical Report 95-20), Amherst, MA: University of Massachusetts, Department of Computer Science.
• Callan, J.P. (1993). Knowledge-based feature generation for inductive learning. Doctoral dissertation, Department of Computer Science, University of Massachusetts, Amherst, MA.
• Fawcett, Tom E. (1993). Feature discovery for problem solving systems. Doctoral dissertation, Department of Computer Science, University of Massachusetts, Amherst, MA.
• Clouse, J.A. (1992). Learning application coefficients with a Sigma-Pi unit. Master's thesis, Computer Science Department, University of Massachusetts, Amherst, MA.
• Saxena, S. (1991). Predicting the effect of instance representations on inductive learning. Doctoral dissertation, Department of Computer Science, University of Massachusetts, Amherst, MA.
• Utgoff, P.E., & Brodley, C.E. (1991). Linear machine decision trees, (COINS Technical Report 91-10), Amherst, MA: University of Massachusetts, Department of Computer and Information Science. (.ps), (.pdf)
• Saxena, S., & Utgoff, P.E. (1990). A new set cover heuristic, (TR-90-5), Amherst, MA: University of Massachusetts, Computer and Information Science Department.
• Saxena, S., & Utgoff, P.E. (1988). A relationship between classification accuracy and search quality, (Coins Technical Report 88-104), Amherst, MA: University of Massachusetts, Department of Computer and Information Science.
• Utgoff, P.E., & Saxena, S. (1987). A perfect lookup table evaluation function for the eight-puzzle, (COINS Technical Report 87-71), Amherst, MA: University of Massachusetts, Department of Computer and Information Science.