Concept Formation. Knowledge and Experience in Unsupervised Learning


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Organized into three parts encompassing 15 chapters, this book begins with an overview of inductive concept learning in machine learning and psychology, with emphasis on issues that distinguish concept formation from more prevalent supervised methods and from numeric and conceptual clustering. This text then describes the cognitive consistency of two concept formation systems that are motivated by a rational analysis of human behavior relative to a variety of psychological phenomena.

Other chapters consider the merits of various schemes for representing and acquiring knowledge during concept formation.

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Concept Formation: Knowledge and Experience in Unsupervised Learning

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This demonstrates that our approach can effectively help users in deciding the final structure of the hierarchy. In case users are not satisfied, they can easily Object O5 and cluster C4 are merged into cluster C3. The change some of these parameters because they can under- generality of the partition fC2 ; C3 g is 0. The process stops stand the effect that these changes will have in the resulting because the generality is positive.

Note that the score hierarchy. The first In our second experiment with artificial data, we experiments show how the generality measure effectively generated 1, instances of a data set containing 10 sym- helps in discovering meaningful levels in a hierarchy by bolic features with six values each. The data set was tuning the parameter. In order to be able to represent this sort of disjunctive algorithm.

http://mrstevenhung.com/xiqid-smartphone-tracking.php To make Smith in psychological experiments [12]. The interest of this the problem more complex, each rule included four data set stems from the fact that it exhibits an underlying conjunctions containing one internal disjunction each, for hierarchical structure of three levels with two, four, and instance: eight classes, respectively.

Using these values, 0. Clusters at the number of clusters. Clearly, partitions into two, four, and top level did not correspond exactly to the two-class eight clusters are the ones showing a more balanced general- division, classifying correctly only a 73 percent of the ity among its components, thus suggesting the set of objects. In fact, all the disjunctive rules in the domain, classifying correctly using this set of parameters, the system recovered a almost 90 percent of objects on average.

The values for with four clusters and correctly classified only a 68 percent were selected according to their V C scores. It is worth to of the objects, thus demonstrating that the two classes of noting that each additional level has not necessarily to this domain were too complex to be discovered at very high significantly increased the size of the tree. For instance, levels.

However, as opposed to the GCF algorithm, adding a new value may either result in a single merging COBWEB was not able to create intermediate nodes and, therefore, add just an additional node to the hierarchy, corresponding to the disjunctions in the rules. We repeated or select, a full set of new nodes. The result depends on the the experiment with simpler domains by reducing the proximity of the values selected.

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We predicted the class of number of disjunctive rules. In each case, the GCF algorithm each unseen instance returning the modal class value found was able to find the level representing each individual in the deepest node reached. Obviously, labels were used disjunction as one of the better balanced levels as regards only for testing, but hidden during training. The The conclusions of this experiment are twofold. First, the system is intended to build a complete hierarchy auto- GCF scheme appears to be guided by different biases than matically and does not provide a mean for selecting levels.

COBWEB and, probably, as it is well-known in supervised Some extensions to COBWEB that are able to make settings, none of the algorithms is superior for every predictions at different levels of the hierarchy have been domain. Second, this experiment demonstrates the limita- proposed, but they are not useful for the label prediction tion of fixed trade-off approaches. Probably, users might task used in our experiments. The method different levels and choose the more suitable one. To assess the performance of the GCF algorithm in This method cannot be applied in our experiments since the nonartificial data, we ran experiments on data sets obtained target feature the label is hidden during training.

The system describes the nodes in the to be inadequate. An alternative is to use the descriptions hierarchy using only their characteristic properties, which stored at each node and an objective function or similarity are property values whose conditional probabilities exceed measure to predict the class of the objects in a separate a predetermined threshold.

When sorting an instance to testing set.

Concept Formation. Knowledge and Experience in Unsupervised Learning Concept Formation. Knowledge and Experience in Unsupervised Learning
Concept Formation. Knowledge and Experience in Unsupervised Learning Concept Formation. Knowledge and Experience in Unsupervised Learning
Concept Formation. Knowledge and Experience in Unsupervised Learning Concept Formation. Knowledge and Experience in Unsupervised Learning
Concept Formation. Knowledge and Experience in Unsupervised Learning Concept Formation. Knowledge and Experience in Unsupervised Learning
Concept Formation. Knowledge and Experience in Unsupervised Learning Concept Formation. Knowledge and Experience in Unsupervised Learning
Concept Formation. Knowledge and Experience in Unsupervised Learning Concept Formation. Knowledge and Experience in Unsupervised Learning
Concept Formation. Knowledge and Experience in Unsupervised Learning Concept Formation. Knowledge and Experience in Unsupervised Learning
Concept Formation. Knowledge and Experience in Unsupervised Learning Concept Formation. Knowledge and Experience in Unsupervised Learning
Concept Formation. Knowledge and Experience in Unsupervised Learning

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