Reference health
A weighted nearest neighbor algorithm for learning with symbolic features
18/18 checkable references clean · checked 2026-08-29
Every reference with a DOI in the deposited reference list resolved to a known work in Crossref or DataCite at the dated check, and none carried a retraction, withdrawal, or removal notice.
23 without a DOI — not checked. A reference deposited without a DOI is never matched by title or guessed at; it stays outside the checked set, and this line discloses that.
The 18 checked references that resolve
resolves10.1016/0022-2836(78)90297-8
Analysis of the accuracy and implications of simple methods for predicting the secondary structure of globular proteins
resolves10.1017/S0140525X00079760
What connectionist models learn: Learning and representation in connectionist networks
resolves10.1002/bip.360221211
Dictionary of protein secondary structure: Pattern recognition of hydrogen‐bonded and geometrical features
resolves10.1145/32206.32207
ARIADNE: pattern-directed inference and hierarchical abstraction in protein structure recognition
resolves10.1016/0022-2836(74)90405-7
Algorithms for prediction of α-helical and β-structural regions in globular proteins
resolves10.1016/0005-2795(75)90109-9
Comparison of the predicted and observed secondary structure of T4 phage lysozyme
resolves10.1016/0022-2836(88)90564-5
Predicting the secondary structure of globular proteins using neural network models
The 23 references without a DOI — listed, not checked
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no DOI — not checkedAha, D. (1990). A study of instance-based algorithms for supervised learning tasks. Doctoral dissertation, Department of Information and Computer Science, University of California, Irvine. Technical Report 90-42.
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no DOI — not checkedChou, P. & Fasman, G. (1978). Prediction of the secondary structure of proteins from their amino acid sequence.Advanced Enzymology, 47, 45?148.Biochemistry, 13, 222?245.
no DOI — not checkedCost, S. (1990). Master's thesis. Department of Computer Science, Johns Hopkins University.
no DOI — not checkedCost, S. & Salzberg, S. (1990). Exemplar-based learning to predict protein folding.Proceedings of the Symposium on Computer Applications to Medical Care (pp. 114?118). Washington, DC.
no DOI — not checkedCrick, F. & Asanuma, C. (1986). Certain aspects of the anatomy and physiology of the cerebral cortex. In J. McClelland, D. Rumelhart, & the PDP Research Group (Eds.),Parallel distributed processing: Explorations in the microstructure of cognition (Vol. II). Cambridge, MA: MIT Press.
no DOI — not checkedDietterich, T., Hild, H., & Bakiri, G. (1990). A comparative study of ID3 and backpropagation for English text-to-speech mapping.Proceedings of the 7th International Conference on Machine Learning (pp. 24?31), San Mateo, CA: Morgan Kaufmann.
no DOI — not checkedFertig, S. & Gelernter, D. (1991). FGP: A virtual machine for acquiring knowledge from cases.Proceedings of the 12th International Joint Conference on Artificial Intelligence (pp. 796?802). Los Altos, CA: Morgan Kaufmann.
no DOI — not checkedFisher, D. & McKusick, K. (1989). An empirical comparison of ID3 and backpropagation.Proceedings of the International Joint Conference on Artificial Intelligence (pp. 788?793) San Mateo, CA: Morgan Kaufmann.
no DOI — not checkedKontogiorgis, S. (1988). Automatic letter-to-phoneme transcription for speech synthesis (Technical Report JHU-88/22). Department of Computer Science, Johns Hopkins University.
no DOI — not checkedMcClelland, J. & Rumelhart, D. (1986). A distributed model of human learning and memory. In J. McClelland, D. Rumelhart, & the PDP Research Group (Eds.),Parallel distributed processing: Explorations in the microstructure of cognition (Vol. II). Cambridge, MA: MIT Press.
no DOI — not checkedMooney, R., Shavlik, J., Towell, G., & Gove, A. (1989). An experimental comparison of symbolic and connectionist learning algorithms.Proceedings of the International Joint Conference on Artificial Intelligence (pp. 775?780). San Mateo, CA: Morgan Kaufmann.
no DOI — not checkedNosofsky, R. (1984). Choice, similarity, and the centext theory of classification.Journal of Experimental Psychology: Learning, Memory, and Cognition 10(1, 104?114.
no DOI — not checkedSalzberg, S. (1989). Nested hyper-rectangles for exemplar-based learning. In K.P. Jantke (Ed.),Analogical and Inductive Inference: International Workshop AII '89. Berlin: Springer-Verlag.
no DOI — not checkedSalzberg, S. (1991). A nearest hyperrectangle learning method.Machine Learning, 6(3, 251?276.
no DOI — not checkedSejnowski, T. & Rosenberg, C. (1987). NETtalk: A parallel network that learns to read aloud.Complex Systems I 145?168. (Also Technical Report JHU/EECS-86/01. Baltimore, MD: John Hopkins University.
no DOI — not checkedShavlik, J., Mooney, R., & Towell, G. (1989). Symbolic and neural learning algorithms: an experimental comparison (Technical Report #857). Madison, WI: Computer Sciences Department, University of Wisconsin.
no DOI — not checkedSigillito, V. (1989). Personal communication.
no DOI — not checkedTowell, G., Shavlik, J., & Noordewier, M. (1990). Refinement of approximate domain theories by knowledge-based neural networks.Proceedings Eighth National Conference on Artificial Intelligence (pp. 861?866). Menlo Park, CA: AAAI Press.
no DOI — not checkedWaltz, D. (1990). Massively parallel AI.Proceedings Eighth National Conference on Artificial Intelligence (pp. 1117?1122). Menlo Park, CA: AAAI Press.
no DOI — not checkedWeiss, S. & Kapouleas, I. (1989). An empirical comparison of pattern recognition, neural nets, and machine learning classification methods.Proceedings of the International Joint Conference on Artificial Intelligence (pp. 781?787). San Mateo, CA: Morgan Kaufmann.
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