Steven Salzberg. A nearest hyperrectangle learning method. Machine Learning, 6(3):251--276, 1991. Home/Search Document Not in Database Summary Related Articles Check |
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Learning Weight Intervals for Classification - Bridge, Ferguson (Correct)
....contribution of less relevant, including redundant, attributes. We look now at one way of learning such a weight vector. 3 Learning a Weight Vector There are many weight learning algorithms. We choose to use an online optimizer [9] An early example of such a learner is Salzberg s EACH algorithm [8]. This in uenced Aha s IB4 [1] And this, in turn, in uenced Kira s and Rendell s RELIEF algorithm [5] which removed IB4 s assumption of a uniform distribution of irrelevant attribute values. Kononenko experimented with variants of the RELIEF algorithm [6] A minor variant of Kononenko s RELIEF F ....
Salzberg, S.L.: A Nearest Hyperrectangle Learning Method, Machine Learning, vol.6, pp.251-276, 1991
Batch Learning of Disjoint Feature Intervals - Akkus (1996) (1 citation) (Correct)
....selection method is that it treats features as completely relevant or irrelevant. In reality, the degree of relevance may not be just 0 or 1, but any value between them. Knowledge representation in exemplar based learning models are either representative instances [2, 5] or hyperrectangles [58, 59]. For example, instancebased learning model retains examples in memory as points, and never changes them. The only decisions that are made are what points to store and how to measure similarity. Several variants of this model have been developed [2, 3, 4, 5] Nested generalized exemplars model ....
....examples in memory as points, and never changes them. The only decisions that are made are what points to store and how to measure similarity. Several variants of this model have been developed [2, 3, 4, 5] Nested generalized exemplars model represents the learned knowledge as hyperrectangles [58, 59]. This model changes the point storage model of the instance based learning and retains examples in the memory as axisparallel hyperrectangles. The Classification by Feature Partitioning [27, 28, 65] and Classification with Overlapping Feature Intervals [67] algorithms are also exemplar based ....
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S. Salzberg, A Nearest Hyperrectangle Learning Method, Machine Learning, 6:251-276, 1991.
Non-Incremental Classification Learning Algorithms Based On.. - Demiröz (Correct)
....memory verbatim, with no change of representation. An example is defined as a vector of feature values along with a label which represents the category (class) of the example. Knowledge representation of exemplar based models can be maintained as representative instances [2, 5] hyperrectangles [62, 63], or feature projection based representations [7, 8, 22, 32, 73] Unlike Explanation Based Generalization (EBG) 19, 50] little or no domain specific knowledge is required in exemplar based learning. Figure 2.1 presents a hierarchical classification of exemplar based learning models. ....
....exemplar is an axis parallel hyperrectangle that may cover several training examples. These hyperrectangles may overlap or nest. Hyperrectangles are grown during training in an incremental manner. Salzberg implemented NGE in a program called EACH (Exemplar Aided Constructor of Hyperrectangles) [63]. In EACH, the learner compares new examples to those it has seen before and finds the most similar generalized exemplar in memory. NGE theory makes several significant modifications to the exemplar based model. It retains the notion that examples should be stored verbatim in memory, but once it ....
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S. Salzberg, A Nearest Hyperrectangle Learning Method, Machine Learning, 6:251--276, 1991.
On Integrating Inductive Learning with Prior Knowledge and.. - Giraud-Carrier (1994) (Correct)
....of IBL s similarity function [AHA91b] The two metrics are equivalent when a purely instance based learning algorithms is used, and DHET extends IBL s similarity function to inductive learning algorithms that use and or create general rules. DHET is also similar to the distance metric used by NGE [SAL91]. It is somewhat simpler since rules containing don t cares represent hyperplanes rather than hyperrectangles. It is also 69 inherently heterogeneous whereas NGE s distance is designed for purely continuous domains. Extensions to heterogeneous spaces have been proposed (see for example [WET93] ....
....to heterogeneous spaces have been proposed (see for example [WET93] NGE s distance is also a weighted sum, where each attributewise distance is assigned its own weight in the computation of the final distance. Mechanisms to assign weights to attributes in distance computation may be found in [SAL91, STA86, WET93]. DHET could be similarly extended. 5. Evaluation In this section, we give empirical evidence of the adequacy of DHET. The results were obtained by executing the same algorithm, only varying the metric it uses. ILA (Incremental Learning Algorithm) was chosen because of its simplicity, execution ....
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Salzberg, S. (1991). A Nearest Hyperrectangle Learning Method. Machine Learning, 6, 277-309. 137
Abstraction Considered Harmful: Lazy Learning Of Language.. - Daelemans (1996) (Correct)
.... AI, the concept has appeared in several disciplines (from computer vision to robotics) using terminology such as similarity based, example based, memory based, exemplar based, case based, analogical, nearest neighbour, and instance based (Stan ll and Waltz, 1986; Kolodner, 1993; Aha et al. 1991; Salzberg, 1990). Ideas about this type of analogical reasoning can be found also in non mainstream linguistics and pyscholinguistics (Skousen, 1989; Derwing Skousen, 1989; Chandler, 1992; Scha, 1992) In computational linguistics (apart from incidental computational work of the linguists referred to earlier) ....
Salzberg, S. (1990) `A nearest hyperrectangle learning method'. Machine Learning 6, 251-276.
Adaptive Nearest Neighbor Classification using Support.. - Domeniconi, Gunopulos (2001) (7 citations) (Correct)
....J classes and l training observations. The training observations consist of n feature measurements x = x 1 ; Delta Delta Delta ; x n ) 2 n and the known class labels j = 1; J . The goal is to predict the class label of a given query q. The K nearest neighbor classification method [6, 12, 15, 16, 19, 20] is a simple and appealing approach to this problem: it finds the K nearest neighbors of q in the training set, and then predicts the class label of q as the most frequent one occurring in the K neighbors. Such a method produces continuous and overlapping, rather than fixed, neighborhoods and uses ....
S. Salzberg, A Nearest Hyperrectangle Learning Method. Machine Learning 6:251-276, 1991.
DeEPs: A New Instance-based Discovery and Classification System - Li, Dong (2001) (1 citation) (Correct)
....patterns, borders, knowledge discovery, classification. 1 1 Introduction Instance based learning (lazy learning) Aha, 1997) as exemplified by k nearest neighbor (k NN) Cover Hart, 1967) is an extensively and thoroughly studied topic in machine learning (Aha et al. 1991; Dasarathy, 1991; Salzberg, 1991; Zhang, 1992; Langley Iba, 1993; Wettscherech, 1994; Datta Kibler, 1995; Wettschereck Dietterich, 1995; Devroye et al. 1996; Domingos, 1996; Datta Kibler, 1997; Wilson Martinez, 1997; Keung Lam, 2000; Kubat Cooperson, 2000; Wilson Martinez, 2000) The intuition behind the k NN ....
Salzberg, S. (1991). A nearest hyperrectangle learning method. Machine Learning, 6, 251--276.
Locally Adaptive Metric Nearest Neighbor Classification - Domeniconi, Peng, Gunopulos (2002) (3 citations) (Correct)
....and N training observations. The training observations consist of q feature measurements x = x 1 ; Delta Delta Delta ; x q ) 2 q and the known class labels, L j , j = 1; J . The goal is to predict the class label of a given query x 0 . The K nearest neighbor classification method [6, 11, 12, 13, 16, 17] is a simple and appealing approach to this problem: it finds the K nearest neighbors of x 0 in the training set, and 1 then predicts the class label of x 0 as the most frequent one occurring in the K neighbors. Such a method produces continuous and overlapping, rather than fixed, neighborhoods ....
S. Salzberg, A Nearest Hyperrectangle Learning Method. Machine Learning 6:251-276, 1991.
k. Results indicate that this procedure is very effective .. - Banded Sinusoidal Tasks (1994) (Correct)
....with each instance, and uses only those instances with good records during prediction. Instances with very poor prediction records are deleted from the set of stored exemplars. IB4 is an extension of the IB3 algorithm that also learns feature weights in a manner similar to that used by Salzberg [Sal91] see also Chapter 4, Section 5.7) Aha [Aha90] reports substantially superior performance for IB4 over IB1 in the presence of many irrelevant features. However, this superior performance was only obtained when k was kept fixed at k = 1 for IB1. This confirms the results from Section 3 where we ....
....by constructing hyperrectangles that represent a collection of training examples that belong to the same class. More compact representations of the training data lead to faster classification times and may increase the ability of the user to understand decisions made by the classifier. Salzberg [Sal91] describes a family of learning algorithms based on nested generalized exemplars (NGE) In NGE, an exemplar is a single training example, and a generalized exemplar is an axis parallel hyperrectangle that may cover several training examples. These hyperrectangles may overlap or nest. The NGE ....
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S. Salzberg. A nearest hyperrectangle learning method. Machine Learning, 6:277--309, 1991.
About Breaking the Trade Off Between Accuracy and.. - Merckt, Decaestecker (1995) (Correct)
....resulting class. To approximate a PR drawn by the ICR, GEM uses orthogonal hyperrectangles under the form of intervals defined over the instance space. This approach is widely used by Symbolic Learning systems (see for example ID3 [Quinlan, 1986] AQ [Michalski, 1983] or Nearest Hyperrectangles [Salzberg, 1991]) because of their natural understanding: an orthogonal box can be easily represented by a conjunctive rule where each term tests a cut point value of an attribute. Interpretation Biases Each PR of the ICR is approximated by a set of closed geometrical figures. There are two basic biases used ....
Salzberg Steven (1991) A Nearest Hyperrectangle Learning Method. Machine Learning vol. 6, n 3, May 1991, Kluwer Academic Publishers.
Knowledge-Rich Similarity-Based Classification - Timo Steens Institute (Correct)
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Steven Salzberg. A nearest hyperrectangle learning method. Machine Learning, 6(3):251--276, 1991.
Partial and Vague Knowledge for Similarity Measures - Steffens (2005) (Correct)
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Steven Salzberg. A nearest hyperrectangle learning method. Machine Learning, 6(3):251--276, 1991.
An Integrated Framework for Learning and Reasoning - Giraud-Carrier, Martinez (1995) (Correct)
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Salzberg, S. #1991#. A nearest hyperrectangle learning method. Machine Learning, 6, 251#276.
Knowledge-Rich Similarity-Based Classification - Steffens (Correct)
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Steven Salzberg. A nearest hyperrectangle learning method. Machine Learning, 6(3):251--276, 1991.
Selecting and Weighting Features Using a Genetic Algorithm.. - Beddoe, Petrovic (2006) (110 citations) (Correct)
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S Salzberg. A nearest hyperrectangle learning method. Machine Learning, 6:251--276, 1991.
Inductive Generalisation in Case-Based Reasoning Systems - Griffiths (1996) (1 citation) (Correct)
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S Salzberg. A nearest hyperrectangle learning method. Machine Learning, 6:251--276, 1991.
Possibilistic Instance-Based Learning - Hüllermeier (Correct)
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S. Salzberg. A nearest hyperrectangle learning method. Machine Learning, 6:251--276, 1991.
Using a Relevance Model for Performing Feature Weighting - Merida-Campos, Rollon (Correct)
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S.Salzberg. A Nearest Hyperrectangle Learning Method. Machine Learning, 6:251-276. 1991.
Context-Aware Personalised Service Delivery - Pignotti And Edwards (Correct)
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S. Salzberg, A nearest hyperrectangle learning method., volume 6, 277--309, 1991.
Investigating Different Methods for Efficient.. - Maximini.. (2003) (Correct)
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S Salzberg. A nearest hyperrectangle learning method. Machine Learning, 6:277--309, 1991.
Efficient Local Flexible Nearest Neighbor Classification - Domeniconi, Gunopulos (Correct)
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S. Salzberg, A Nearest Hyperrectangle Learning Method. Machine Learning 6:251-276, 1991.
The PNC 2 Cluster Algorithm - An integrated learning algorithm.. - Haendel (2003) (Correct)
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Steven Salzberg. A nearest hyperrectangle learning method. In Proc. 6th International Conference on Machine Learning, pages 251--276, 1991.
Center-Based Indexing in Vector and Metric Spaces - Wojna (2003) (Correct)
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Salzberg, S. (1991). A nearest hyperrectangle learning method. Machine Learning, 2, 229-246.
Determining Feature Weights Using a Genetic Algorithm in a.. - Beddoe, Petrovic (2004) (Correct)
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S Salzberg. A nearest hyperrectangle learning method. Machine Learning, 6:251--276, 1991.
Approximative Retrieval of Attribute Dependent Generalized.. - Maximini, Tartakovski (Correct)
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S Salzberg. A nearest hyperrectangle learning method. Machine Learning, 6:277--309, 1991.
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