Classifier systems are increasingly being applied to the analysis of economic phenomena. Among these applications are adaptive models of learning, the creation of artifical economies, and the development of economic webs. A methodology is described for studying the dynamical behavior of classifier systems. The methodology is useful because of the current lack of analytical results describing in...

KL-ONE is o system for representing knowledge in Artificial Intelligence progroms. It has been developed and refined over o long period ond hos been used in both basic research and implemented knowledge-based systems in o number of places in the Al community. Here we present the kernel ideas of KL-ONE, emphasizing its ability to form complex structured descriptions. In oddition to detoiling oil...

We present parallel algorithms for building decision-tree classifiers on shared-memory multiprocessor (SMP) systems. The proposed algorithms span the gamut of data and task parallelism. The data parallelism is based on attribute scheduling among processors. This basic scheme is extended with task pipelining and dynamic load balancing to yield faster implementations. The task parallel approach u...

This paper presents fast scalable decision-tree-based classification algorithms targeting shared-memory systems. The algorithms are based on the sequential SPRINT classifier and span the gamut of data and task parallelism. The data parallelism is based on attribute scheduling among processors. This is extended with task pipelining and dynamic load balancing to yield more efficient schemes. The ...

This paper presents a system which uses a combination of multiple text similarity measures of varying complexities to classify Quora question pairs as duplicate or different. The solution uses a support vector classifier model trained using the precomputed features ranging from longest common sub-string and sub sequences to word similarity based on lexical and semantic resources. The scope of t...

In this paper, the naive credal classifier, which is a set-valued counterpart of naive Bayes, is extended to a general and flexible treatment of incomplete data, yielding a new classifier called naive credal classifier 2 (NCC2). The new classifier delivers classifications that are reliable even in the presence of small sample sizes and missing values. Extensive empirical evaluations show that, ...

The major aim of this article is modeling of nonlinear systems with friction structure that, thismethod is essentially extended based on taylore expansion polynomial. So in this study, thetaylore expansion was extended in the generalized form for the differential equations of the statespaceform. The proposed structure is based on multi independent variables taylore extended.According to the pro...

This paper discusses a new computational scheme based on Functional Networks and applies it to the problem of classification and quantification of gas species in a mixture. A Generalized Functional Network as a new classifier is proposed in order to improve the potentialities of the standard Functional Network classifier. Both methodology and learning algorithm are derived. The performance of t...

Collective classification algorithms have been used to improve classification performance when network training data with content, link and label information and test data with content and link information are available. Collective classification algorithms use a base classifier which is trained on training content and link data. The base classifier inputs usually consist of the content vector ...