18058814
Learning Rules from Very Large Databases Using Rough Multisets
This paper presents a mechanism called LERS-M for learning production rules from very large databases. It can be implemented using object-relational database systems, it can be used for distributed data mining, and it has a structure that matches well with parallel processing. LERS-M is based on rough multisets and it is formulated using relational operations with the objective to be tightly coupled with database systems. The underlying representation used by LERS-M is multiset decision tables, which are derived from information multisystems. In addition, it is shown that multiset decision tables provide a simple way to compute Dempster-Shafer’s basic probability assignment functions from. data sets.
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
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.
206954852
A feedback control circuit design technique to suppress power noise in high speed output driver
In today's sub-micron CMOS integrated circuit technology, high speed output switching signals interacting with external inductance and capacitance produce noise which contaminates output signals and power buses. A Feedback Control Slew Rate Output Driver (FCSROD) which reduces the noise spike down to approximately 64% of a conventional output buffer without incurring the penalty of the propagation delay and even the rise/fall time is described. This effective power noise suppression is achieved by using distributed and weighted switching driver segments in conjunction with feedback control to control the output driver's slew rate. Dynamic short circuit current which is generated while both pFET and nFET are conducting is also minimized to reduce di/dt noise. FCSROD was compared with a conventional and the controlled slew rate output buffer, showing 64% noise reduction comparing to the conventional driver, and 22% improvement in both propagation delay and rise/fall time comparing with the controlled slew rate output driver.
9764502
On Improved Least Flexibility First Heuristics Superior for Packing and Stock Cutting Problems
Two dimensional cutting and packing problems have applications in many manufacturing and job allocation problems. In particular, in VLSI floor planning problems and stock cutting problems, many simulated annealing and genetic algorithms based methods have been proposed in the last ten years. These researches have mainly been focused on finding efficient data structures for representing packing results so the search space and processing time of the underlying search engine can be minimized. In this paper, we tackle the problem from a different approach. Instead of using stochastic searches, we introduce an effective deterministic optimization algorithm for packing and cutting. By combining an improved Least Flexibility First principle and a greedy search based evaluation routine, we can obtain very encouraging results: In stock cutting problems, our algorithm achieved over 99% average packing density for a series of public rectangle packing data sets, which is significantly better than the 96% packing density obtained by meta-heuristics (simulated annealing) based results while using much less CPU time; whereas in rectangle packing applying the well-known MCNC and GSRC benchmarks, we achieved the best (over 96%) packing density among all known published results packed by other methods. Our encouraging results seem to suggesting a new experimental direction in designing efficient deterministic heuristics for some kind of hard combinatorial problems.
7674871
Contextual vector quantization for speech recognition with discrete hidden Markov model
Abstract By using formulation of the finite mixture distribution identification, in this paper, several alternatives to the conventional LBG VQ method are investigated. A contextual VQ method based on the Markov Random Field (MRF) theory is proposed to model the speech feature vector space. Its superiority is confirmed by a series of comparative experiments in a speaker independent isolated word recognition task by using different VQ schemes as the front-end of DHMM. The motivation to use MRF to model the contextual dependence information in the underlying speech production process can be readily extended to acoustic modeling of the basic speech units in speech recognition.
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%. >