Topic
Decision tree model
About: Decision tree model is a research topic. Over the lifetime, 2256 publications have been published within this topic receiving 38142 citations.
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20 Sep 2004
TL;DR: An experimental comparison using well-known real world data sets, chosen to provide a variety of clustering scenarios, showed the new approach produced more reliable performances.
Abstract: Subtree retraining applied to a Stochastic Competitive Evolutionary Neural Tree model (SCENT) is introduced. This subtree retraining process is designed to improve the performance of the original model which provides a hierarchical classification of unlabelled data. The effect of subtree retraining on the network produces stable classificatory structures by repeatedly restructuring the weakest branch of the classification tree based on internal relation between members. An experimental comparison using well-known real world data sets, chosen to provide a variety of clustering scenarios, showed the new approach produced more reliable performances.
06 Oct 1993
TL;DR: Although the problem of deciding whether a given goal has a successful SLD-derivation (the SUCCESS problem) is decidable for the classes studied, it turns out to be NP-complete even for some very simple classes.
Abstract: In this paper we consider a few simple classes of definite programs and goals and study the problem of deciding whether a given goal has a successful SLD-derivation (the SUCCESS problem). Although the problem is always decidable for the classes studied, it turns out to be NP-complete even for some very simple classes.
Patent•
29 Jan 2014
TL;DR: In this paper, a hierarchical encryption agent channel detection method is proposed, which includes: S1, using a training set to train a decision tree and an artificial immunity model both required by detection; S2, recognizing an agent channel network flow from background traffic, recognizing a hidden protocol in the agent channels by the decision tree model obtained by training, and detecting illegal contents by the Artificial Immunity model.
Abstract: The invention provides a hierarchical encryption agent channel detection method. The method includes: S1, using a training set to train a decision tree and an artificial immunity model both required by detection; S2, recognizing an agent channel network flow from background traffic, recognizing a hidden protocol in the agent channels by the decision tree model obtained by training, and detecting illegal contents by the artificial immunity model. The method has the advantages that the hierarchical processing structure adopted, challenges brought with high traffic can be dealt with effectively, the problems caused by encryption and confidentiality of the encrypted agent channel protocol can also be solved, and technical support can be provided for the design and implementation of high-performance traffic sorting systems and content monitor systems in high-speed networks.
01 Jan 2008
TL;DR: A novel approach to variable complexity encoding is proposed, capable of providing a significant, predictable reduction in computational complexity with only a small loss of video quality.
Abstract: †† Summary Variable-complexity algorithms provide a means of managing the computational complexity of a software video CODEC. The reduction in computational complexity provided by existing variable-complexity algorithms depends on the video scene characteristics and is difficult to predict. A novel approach to variable complexity encoding is proposed in this paper. A variable complexity DCT algorithm will be updated adaptively in order to maintain a near-constant computational complexity. This adaptive update algorithm will be capable of providing a significant, predictable reduction in computational complexity with only a small loss of video quality. The proposed approach may be particularly useful for software-only video encoding.