1. H. J. Holz, K. Hofmann and C. Reed, “Chapter 2: Unobtrusive User Modeling for Adaptive Hypermedia, in Personalization Techniques and Recommender Systems”, vol. 70, G. Uchyigit and M. Y. Ma, Eds. World Scientific, 2008, 61-83.
  2. H. J. Holz, K. Hofmann and C. Reed, “Unobtrusive User Modeling for Adaptive Hypermedia”, International Journal of Pattern Recognition and Artificial Intelligence, vol. 21, no. 2, 225-244, 2007.
  3. H. Holz, A. G. Applin and W. Joel, “Status report of the SIGCSE committee on teaching computer science research methods”, in SIGCSE, 2007, 327-328.
  4. A. G. Applin, H. J. Holz, W. Joel, I. Okoye, K. Deibel, B. Grasser, B. J. Oates and G. Wood, “A multi-perspective digital library to facilitate integrating teaching research methods across the computing curriculum”, SIGCSE Bulletin, vol. 39, no. 4, 184-203, 2007.
  5. M. Jain, H. Holz, J. Shrager, O. Vallon, C. Hauser and A. Grossman, “A Hybrid, Recursive Algorithm for Clustering Expressed Sequence Tags in Chlamydomonas reinhardtii”, in ICPR (3), 2006, 404-407.
  6. K. Hofmann, C. Reed and H. J. Holz, “Unobtrusive Data Collection for Web-Based Social Navigation”, in Proceedings of the Workshop on Social Navigation and Community-Based Adaptive Technologies, AH ’06, 2006.
  7. H. J. Holz, A. Applin, B. Haberman, D. Joyce, H. Purchase and C. Reed, “Research methods in computing: what are they, and how should we teach them?”, SIGCSE Bulletin, vol. 38, no. 4, 96-114, 2006.
  8. J. Beidler, H. Holz, K. Yasuhara and E. J. Adams, “The many facets of diversity”, in Proceedings of the 36th SIGCSE Technical Symposium on Computer Science Education, 2005, 558-559.
  9. T. Henry, H. J. Holz, C. Steinback, C. Reed and A. Baid, “Work in progress-student retention and recruitment in computer science programs”, in Frontiers in Education 2004, 2004, vol. 2, F3C - 13-14.
  10. J. Farrell and H. J. Holz, “Enki: open infrastructure for adaptive digital libraries”, in Proceedings of the ACM/IEEE Joint Conference on Digital Libraries, 2002, 391.
  11. H. J. Holz and M. H. Loew, “Validation of relative feature importance using natural data”, Pattern Recognition Letters, vol. 23, no. 4, 367-380, 2002.
  12. H. J. Holz, D. B. Russakoff, H. Abbasi, D. H. Kim, G. Steinberg and R. Shahidi, “Expected versus observed error in a computer-aided navigation system for spine surgery”, in Proceedings of the 15th International Congress and Exhibition on Computer Assisted Radiology and Surgery, 2001, 112-116.
  13. H. Abbasi, R. P. Grzeszczuk, S. Chin, H. J. Holz, S. Hariri, R. Badr, D. H. Kim, J. R. A. Jr. and R. Shahidi, “Development of fluoroscopic registration in spinal neuronavigation”, in Proceedings of the 15th International Congress and Exhibition on Computer Assisted Radiology and Surgery, 2001, 1236-1238.
  14. H. J. Holz and M. H. Loew, “Validation of Relative Feature Importance Using a Natural Data Set”, in Proceedings of the 15th International Conference on Pattern Recognition, 2000, 2414-2417.
  15. H. J. Holz and M. H. Loew, “Design Choices and Theoretical Issues for Relative Feature Importance, a Metric for Nonparametric Discriminatory Power”, in SSPR/SPR, 2000, 696-705.
  16. R. P. Grzeszczuk, S. Chin, R. Fahrig, H. Abbasi, H. J. Holz, D. H. Kim, J. R. A. Jr. and R. Shahidi, “A Flouroscopic X-Ray Registration Process for Three-Dimensional Surgical Navigation”, in MICCAI, 2000, 551-556.
  17. H. J. Holz, “Classifier-Independent Feature Analysis”, Washington, DC, 1999.
  18. H. J. Holz and M. H. Loew, “Multi-class classifier-independent feature analysis”, Pattern Recognition Letters, vol. 18, no. 11, 1219-1224, 1997.
  19. H. J. Holz and M. H. Loew, “Concurrency in Feature Analysis”, in Applied Parallel Computing, Computations in Physics, Chemistry and Engineering Science, 1996, 313-322.
  20. H. J. Holz and M. H. Loew, “Relative Feature Importance: A Classifier-Independent Approach to Feature Selection”, in Pattern Recognition in Practice IV, E. S. Gelsema and L. N. Kanal, Eds. Amsterdam: Elsevier, 1994, 473-487.
  21. H. J. Holz and M. H. Loew, “Non-parametric discriminatory power”, in Proceedings of the 1994 IEEE-IMS Workshop on Information Theory and Statistics, 1994, 65.
  22. C. D. Martin and H. J. Holz, “Non-apologetic computer ethics education: a strategy for integrating social impact and ethics into the computer science curriculum”, in Teaching computer ethics, T. W. Bynum, W. Maner and J. L. Fodor, Eds. New Haven, CT, USA: Southern Connecticut State University, 1992, 50-66.
  23. C. D. Martin and H. J. Holz, “Integrating Social Impact and Ethics Issues Across the Computer Science Curriculum”, Education and Society, vol. 2, 137-143, 1992.
  24. G. Bailey, R. Demeo, H. J. Holz and J. Revell, “Survival of the Fittest”, in Proceedings of the Workshop on Evolution and Chaos in Cognitive Processing, International Joint Conference on Artificial Intelligence, 1991.
  25. P. Bock, H. Holz, R. Rovner and C. J. Kocinski, “An initial performance evaluation of unsupervised learning with ALIAS”, in International Joint Conference on Neural Networks, 1990, vol. 1, 451-465.
  26. P. Bock, R. Rovner, C. J. Kocinski, H. J. Holz and G. Becker, “A parallel implementation of collective learning systems theory: Adaptive Learning Image Analysis System (ALIAS)”, in CSC ’90: Proceedings of the 1990 ACM annual conference on Cooperation, 1990, 457-469.
  27. P. Bock, G. Becker, H. J. Holz, C. J. Kocinski and R. Rovner, “An Application of Collective Learning Systems Theory to an Adaptive Learning Image Analysis System: Project ALIAS”, in Neuro-Nimes ’89: International Workshop on Neural Networks and Their Applications, 1989, 1-12.