5119128
From Data to Knowledge: an Integrated Rule-Based Data Mining System
This paper presents an integrated rule-based data mining system that is capable of creating rulebased classifiers with web-based user interface from data sets provided by end users. It provides a streamlined integration of three technologies, namely, database systems, machine learning systems , and rule-based systems. Rules generated by the system are based on rough set theory. Thus, it is capable of dealing with uncertain rules. The system generates user interface dynamically, therefore, end users are released from the burden of programming. The generated classifiers are stored in a database system and are delivered as web-based applications. Therefore, they can be managed and accessed easily by using web browsers.
12408507
A Rough Set Approach to Attribute Generalization in Data Mining
Abstract This paper presents a method for updating approximations of a concept incrementally. The results can be used to implement a quasi-incremental algorithm for learning classification rules from very large data bases generalized by dynamic conceptual hierarchies provided by users. In general, the process of attribute generalization may introduce inconsistency into a generalized relation. This issue is resolved by using the inductive learning algorithm, LERS based on rough set theory.
18544921
Active User-Based and Ontology-Based Web Log Data Preprocessing for Web Usage Mining
User identification and session identification are two major steps in preprocessing Web log data for Web usage mining. This paper introduces a fast active user-based user identification algorithm with time complexity O(n). The algorithm uses both an IP address and a finite users' inactive time to identify different users in the Web log. Web site ontology is useful for identifying Web site structure and break points for browsing behavior. For session identification, we present an ontology-based method that utilizes the Web site structure and functionalities to identify different sessions
6473108
N-th Order Ergodic Multigram HMM for Modeling of Languages without Marked Word Boundaries
Ergodic HMMs have been successfully used for modeling sentence production. However for some oriental languages such as Chinese, a word can consist of multiple characters without word boundary markers between adjacent words in a sentence. This makes word-segmentation on the training and testing data necessary before ergodic HMM can be applied as the language model. This paper introduces the N-th order Ergodic Multigram HMM for language modeling of such languages. Each state of the HMM can generate a variable number of characters corresponding to one word. The model can be trained without word-segmented and tagged corpus, and both segmentation and tagging are trained in one single model. Results on its application on a Chinese corpus are reported.
894881
Performance improvement of TCP Vegas over heterogeneous networks
Current IP network has become the dominant paradigm for all networking environments. The significant cause of packet losses in such heterogenous networks is no longer limited to network congestion. Traditional TCP interprets every packet loss as caused by congestion which may be not the case in the current Internet. Misinterpretation of wireless random loss as an indication of network congestion results in TCP slowing down its sending rate unnecessarily. We propose a new variant of TCP Vegas named RedVegas. By using the innate nature of Vegas and congestion indications marked by routers, RedVegas may detect random packet losses precisely. Through the packet loss differentiation, RedVegas reacts appropriately to the losses, and therefore the throughput of connection over heterogeneous networks can be significantly improved.
206413943
Test Generation with Dynamic Probe Points in High Observability Testing Environment
High observability testing environment allows internal circuit nodes to be used as test points. However, such flexibility requires the development of new ATPG algorithm. Previous reported algorithm does not guarantee full fault-coverage and assumes all internal circuit nodes are test points. The new algorithm described in this paper will generate a full fault-coverage test set for a fanout free combinational circuit. The main characteristic of the algorithm is that it generates test vectors as well as probe points. As a result, the probe points are different for each test vector, and the number of probe points is the minimum for test set generated. Results obtained show that an average of 30% test vector reduction is achieved compared with the conventional test method which uses only output pins as test points.
30635651
A criterion for a multi-dimensional explosion due to a concentrated nonlinear source
Abstract This article studies a multi-dimensional parabolic first initial–boundary value problem with a concentrated nonlinear source. A criterion for its solution to blow up everywhere on the concentrated source in a finite time is given.
33544047
Isolated Word Recognition by Neural Network Models with Cross-Correlation Coefficients for Speech Dynamics
This paper presents an artificial neural network (ANN) for speaker-independent isolated word speech recognition. The network consists of three subnets in concatenation. The static information within one frame of speech signal is processed in the probabilistic mapping subnet that converts an input vector of acoustic features into a probability vector whose components are estimated probabilities of the feature vector belonging to the phonetic classes that constitute the words in the vocabulary. The dynamics capturing subnet computes the first-order cross correlation between the components of the probability vectors to serve as the discriminative feature derived from the interframe temporal information of the speech signal. These dynamic features are passed for decision-making to the classification subnet, which is a multilayer perceptron (MLP). The architecture of these three subnets are described, and the associated adaptive learning algorithms are derived. The recognition results for a subset of the DARPA TIMIT speech database are reported. The correct recognition rate of the proposed ANN system is 95.5%, whereas that of the best of continuous hidden Markov model (HMM)-based systems is only 91.0%. >