15250571
Frequent Itemset Mining Based on Heuristic Two Level Counting
Recently, many enchanced Apriori algorithms have been proposed to efficiently generate all frequent itemsets from datasets in data mining field. Although efficient techniques were presented, those algorithms are either time-consuming or memory-consuming. To address the issue further, a new algorithm, which introduced a novel support counting method, heuristic two level counting, is proposed. HTLC method adopts an improved itemset generating technology in the generation process of low level itemsets, which promotes the production of low level frequent itemsets or candidate itemsets. It also applies a heuristic traversal technology which speeds up one pass over datasets and support counting technology which largely reduces the number of passes over datasets to the generation of high level frequent itemsets. Finally, the experimental results show that it outperforms existing Apriori-like algorithms in mostly datasets
42610771
A Novel Ordering-Based Greedy Bayesian Network Learning Algorithm on Limited Data
27471784
A Novel Greedy Bayesian Network Structure Learning Algorithm for Limited Data
Existing algorithms for learning Bayesian network (BN) require a lot of computation on high dimensional itemsets, which affects accuracy especially on limited datasets and takes up a large amount of time. To alleviate the above problem, we propose a novel BN learning algorithm MRMRG, Max Relevance and Min Redundancy Greedy algorithm. MRMRG algorithm is a variant of K2 algorithm for learning BNs from limited datasets. MRMRG algorithm applies Max Relevance and Min Redundancy feature selection technique and proposes Local Bayesian Increment (LBI) function according to the Bayesian Information Criterion (BIC) formula and the likelihood property of overfitting. Experimental results show that MRMRG algorithm has much better efficiency and accuracy than most of existing BN learning algorithms when learning BNs from limited datasets.
10413997
The Max-Relevance and Min-Redundancy Greedy Bayesian Network Learning Algorithm
Existing algorithms for learning Bayesian network require a lot of computation on high dimensional itemsets which affects reliability, robustness and accuracy of these algorithms and takes up a large amount of time. To address the above problem, we propose a new Bayesian network learning algorithm MRMRG, Max Relevance-Min Redundancy Greedy. MRMRG algorithm is a variant of K2 which is a well-known BN learning algorithm. We also analyze the time complexity of MRMRG. The experimental results show that MRMRG algorithm has much better efficiency. It is also shown that MRMRG algorithm has better accuracy than most of existing learning algorithms for limited sample datasets.
7806615
A Novel Ordering-Based Greedy Bayesian Network Learning Algorithm on Limited Data
Existing algorithms for learning Bayesian network (BN) require a lot of computation on high dimensional itemsets, which affects accuracy especially on limited datasets and takes up a large amount of time. To alleviate the above problem, we propose a novel BN learning algorithm OM- RMRG, Ordering-based Max Relevance and Min Redun- dancy Greedy algorithm. OMRMRG presents an ordering- based greedy search method with a greedy pruning proce- dure, applies Max-Relevance and Min-Redundancy feature selection method, and proposes Local Bayesian Increment function according to Bayesian Information Criterion (BIC) formula and the likelihood property of overfitting. Exper- imental results show that OMRMRG algorithm has much better efficiency and accuracy than most of existing BN learning algorithms on limited datasets.
11096456
Learning Bayesian Networks with Combination of MRMR Criterion and EMI Method
Currently, learning Bayesian Networks (BNs) from data has become a much attention-getting issue in fields of machine learning and data mining. While there exists few efficient algorithms for learning BNs in presence of incomplete data. In this paper, we present a scoring function based on mutual information for evaluating BN structures. To decrease computational complexity, we introduce MRMR criterion into the scoring function, which enables the computation of the scoring function to involve in only two-dimensional mutual information. When the dataset is incomplete, we use EMI method to estimate the Mutual Information (MI) from the incomplete dataset. As for whether a node ordering is manually given or not, we develop two versions of algorithms, named as MRMR- E1 and MRMR-E2 respectively and evaluate them through experiments. The experimental results on Alarm network show good accuracy and efficiency of our algorithms.
16252151
A Topology Optimization for Truss Based on Improved Group Search Optimizer
In this paper, a novel optimization algorithm, named group search optimizer (GSO) with two improvements, is used to truss structure topology optimization. And the improved methods are using harmony memory and adhering to the boundary which can improve the algorithm obviously. Then a simple and effective topology method, discretization of topology variables is incorporated with improved GSO to make sure that the topology optimization works well. In the end of the paper, two topology optimal models, a planar truss and a spatial truss, are used to test this topology methods based on the improved GSO. The results show that the method with improved GSO is feasible and robust for truss topology optimization.
8504259
A Low Complexity MPEG Video Decoder with Arbitrary Downscaling Capability
This paper presents some new techniques to implement arbitrary downscaling in decoding MPEG video with very low complexity. The DCT block recomposition method, which was used in resizing static image, is introduced to downscale video in frequency domain with arbitrary factors. New motion compensation based on the block group motion evaluation is proposed to correspond to the downscaled IDCT. Block groups of regular motion and irregular motion are handled respectively to balance the computation cost and picture quality. Analysis indicates the great complexity reduction and experiments show the decoder performance on the MPEG-4 video sequence. These techniques will be beneficial to the video applications on the mobile devices and many other low end embedded systems
2597775
Re-cinematography: improving the camera dynamics of casual video
This paper presents an approach to post-processing casually captured videos to improve apparent camera movement. Re-cinematography transforms each frame of a video such that the video better follows cinematic conventions. The approach breaks videos into shorter segments. For segments of the source video where the camera is relatively static, re-cinematography uses image stabilization to make the result look locked-down. For segments with camera motions, camera paths are keyframed automatically and interpolated with matrix logarithms to give velocity-profiled movements that appear intentional and directed. The approach automatically balances the tradeoff between motion smoothness and distortion to the original imagery. Results from our prototype show improvements to poor quality home videos.
6983597
A Comparative Study of Parallel Prefix Adders in FPGA Implementation of EAC
Several regular parallel trees have been proposed over the years to optimize logic depth, area, fan-out and in- terconnect count for logic circuits. In this paper, we propose a comparative study of different parallel prefix trees used in the design of a new end-around carry (EAC) adder targeting FPGA technology. This new adder is based on the fast 128-bit binary floating-point EAC adder which has been implemented in the IBM POWER6 microprocessor's fused multiply-add unit. The parallel prefix tree implemented on the IBM's EAC adder is a Kogge-Stone tree which has been chosen for its high performance and its low power consumption. Our comparative study highlights the main performance differences among fourteen different architecture configurations when targeting an FPGA EAC adder design. We focus on the area requirements and the critical path delay of these designs. Our experimental results show that there is one architecture configuration with the lower area requirement and the higher performance.
11582687
Web service enablement of communication services
In this paper, we introduce Web service application session services, WS-session, for Web service based application session management in communication. A two-way Web service framework is described. It supports the use of Web service in the situation of telecommunication where the communication endpoint can be both a client and a peer-to-peer server. We describe the interface design in two-way Web service interaction based on the concept of tightly coupled interface and loosely coupled interface solutions. The proposed approach provides full support of asynchronous outbound operations and event notifications in communication services. It is implemented in a research prototype system that provides both standard based (ECMA-348) services and Avaya service extensions. In addition, we investigated the integration of our proposed approach with BPEL4WS. It allows the user to create new services through a drag-n-drop based service creation environment (ECLIPSE/BPEL4WS). It leads to a paradigm of service creation through Web service composition. More sophisticated telecommunication services are created from composing the basic services.