Cost, S. and Salzberg, S. (1990a). Exemplar-based learning to predict protein folding. In Proc. Sym. Comp. Appl. Medical Care, Washington, DC. Home/Search Document Not in Database Summary Related Articles Check |
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Machine Learning in Molecular Biology Sequence Analysis - Chan (1991) (1 citation) (Correct)
....to the test instance. The classification of the test instance is then based on this selected example(s) Each exemplar in memory is associated with a weight that reflects its effectiveness in correct prediction and is incorporated into the distance metric. This weight is adjusted during training (Cost and Salzberg, 1990b) For example, using the cup and non cup descriptions in the previous section, we can transform Table 1 to Table 2. The binary substitutions are: no = 0 and yes = 1. COLOR and MATERIAL, which are not binary descriptors, are omited from Table 2 for simplicity. Section 4.1.2 describes a method ....
....King s and Seshu et al. s approaches besides the representation of rules. King uses a greedy approach to generate rules to cover the examples while Seshu et al. keep generating a new set of rules based on an enhanced set of features until accuracy cannot be improved. Exemplar based Learning Cost and Salzberg (1990a) used an exemplar based learning approach. Based on the examples, they use the value difference metric (Stanfill and Waltz, 1986) to generate distance tables for each symbolic feature. This metric provides a numeric distance measure between two values of a symbolic feature and is defined as: ....
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Cost, S. and Salzberg, S. (1990a). Exemplar-based learning to predict protein folding. In Proc. Sym. Comp. Appl. Medical Care, Washington, DC.
A Weighted Nearest Neighbor Algorithm for Learning with.. - Cost, Salzberg (1993) (166 citations) Self-citation (Cost Salzberg) (Correct)
.... learning programs (also called exemplar based (Salzberg, 1990) or nearest neighbor (Cover and Hart, 1967) methods) which learn by storing examples as points in a feature space, require some means of measuring distance between examples (Aha, 1989; Aha and Kibler, 1989; Salzberg, 1989; Cost and Salzberg, 1990). An example is usually a vector of feature values plus a category label. When the features are numeric, normalized Euclidean distance can be used to compare examples. However, when the feature values have symbolic, unordered values (e.g. the letters of the alphabet, which have no natural ....
....points to define the edge of the space. Here, only two points are required to define a rule and an exception. The capability becomes even more important for IBL models that store only a subset of the training examples, because it further reduces the number of points which must be stored (Cost and Salzberg, 1990). Given the above discussion, it should be clear that all instances should not be initialized with weights of 1. Consider a system trained on n Gamma 1 instances, now training on the n th . A hierarchy of instance weights has already been constructed through training to represent the structure ....
[Article contains additional citation context not shown here]
Cost, S. and Salzberg, S. (1990) Exemplar-based Learning to Predict Protein Folding. Proceedings of the Symposium on Computer Applications to Medical Care, Washington, D.C., November 1990.
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