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检索条件"任意字段=6th International Conference on Machine Learning and Data Mining in Pattern Recognition"

共 2836 条记录，以下是2431-2440 订阅

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Palmprint matching using pairwise relative angle histogram

Palmprint matching using pairwise relative angle histogram

引用

6th international conference on machine learning and Cybernetics, ICMLC 2007

作者： Li, Fang Leung, Maylor K.H. Sim, Larree T.G. School of Computer Engineering Nanyang Technological University Singapore 639798 Singapore

ISBN: (纸本)142440973X

In this paper, we propose a novel approach to retrieve line-patterns from large databases in a rotation and translation invariant manner, at the same time, tackle broken line problem. Line segments are extracted from an image as primitives. Each local structure is represented by a set of pair-wise angle relationships, which are simple, invariant to translation and rotation, robust to end-point erosion, segment error, and sufficient for discrimination. Experiment showed encouraging results which also implicate that line segments could provide sufficient information for palmprint recognition. ©2007 IEEE.

关键词： pattern matching

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A scalable association rules mining algorithm based on sorting, indexing and triming

A scalable association rules mining algorithm based on sorti...

引用

6th international conference on machine learning and Cybernetics

作者： Chiou, Chuang-Kai Tseng, Judy C. R. Chung Hua Univ Dept Comp Sci & Informat Engn Hsinchu 300 Taiwan

ISBN: (纸本)9781424409723

Apriori is an influential and well-known algorithm for mining association rules. However, the main drawback of Apriori algorithm is the large amount of candidate itemsets it generates. Several hash-based algorithms, such as DHP and MPIP, were proposed to deal with the problem. DHP employs hash functions to,filter out potential-less candidate itemsets. MPIP further improves DHP by employing minimal perfect hashing functions to avoid generation of candidate itemsets. though MPIP results in a very promising mining efficiency, the memory space required in MPIP increases rapidly as the number of items grows. To obtain even better mining efficiency while reducing the memory space required, a Sorting-Indexing-Trimming (SIT) algorithm for mining association rules;is proposed in this paper. SIT uses the sorting, indexing,, and trimming techniques to reduce the amount of itemsets to be considered. then, to utilize both the advantages of Ariori and MPIP, a revised MPIP algorithm is employed to deal with 2-itemsets, and a revised Apriori algorithm to deal with k-itemsets for k>2. though the memory space required in SIT is less than MPIP, from the experiment results, SIT outperforms both Apriori and MPIP.

关键词： data mining association rule Apriori algorithm

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Multiple classifiers based incremental learning algorithm for learning in nonstationary environments

Multiple classifiers based incremental learning algorithm fo...

引用

6th international conference on machine learning and Cybernetics

作者： Muhlbaier, Michael D. Polikar, Robi Rowan Coll Signal Proc & Pattern Recognit Lab Glassboro NJ 08028 USA

ISBN: (纸本)9781424409723

We describe an incremental learning algorithm designed to learn in challenging non-stationary environments, where the underlying data distribution that governs the classification problem changes at an unknown rate. the algorithm is based on a multiple classifier system that generates a new classifier every time a new dataset becomes available from the changing environment. We consider the particularly challenging form of this problem, where we assume that the previously generated data points are no longer available, even if some of those points may still be relevant in the new environment. the algorithm employs a strategic weighting mechanism to determine the error of each classifier on the current data distribution, and then combines the classifiers using a dynamically weighted majority, voting. We describe the implementation details of algorithm, and track its performance as a function of the environment's rate of change. We show that the algorithm is able to track the changing environment, even when the environment changes drastically over a short period of time.

关键词： incremental learning non-stationary learning learn plus ensemble systems multiple classifier systems

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A hybrid algorithm based on evolution strategies and instance-based learning, used in two-dimensional fitting of brightness profiles in galaxy images 1

引用

5th international conference on machine learning and data mining in pattern recognition

作者： Gomez, Juan Carlos Fuentes, Olac INAOE Dept Comp Sci Luis Enrique Erro 1 Puebla 72000 Mexico Univ Texas El Paso Comp Sci Dept El Paso TX 79968 USA

ISBN: (数字)9783540734994

ISBN: (纸本)9783540734987

the hybridization of optimization techniques can exploit the strengths of different approaches and avoid their weaknesses. In this work we present a hybrid optimization algorithm based on the combination of Evolution Strategies (ES) and Locally Weighted Linear Regression (LWLR). In this hybrid a local algorithm (LWLR) proposes a new solution that is used by a global algorithm (ES) to produce new better solutions. this new hybrid is applied in solving an interesting and difficult problem in astronomy, the two-dimensional fitting of brightness profiles in galaxy images. the use of standardized fitting functions is arguably the most powerful method for measuring the large-scale features (e.g. brightness distribution) and structure of galaxies, specifying parameters that can provide insight into the formation and evolution of galaxies. Here we employ the hybrid algorithm ES+LWLR to find models that describe the bi-dimensional brightness profiles for a set of optical galactic images. Models are created using two functions: de Vaucoleurs and exponential, which produce models that are expressed as sets of concentric generalized ellipses that represent the brightness profiles of the images. the problem can be seen as an optimization problem because we need to minimize the difference between the flux from the model and the flux from the original optical image, following a normalized Euclidean distance. We solved this optimization problem using our hybrid algorithm ES+LWLR. We have obtained results for a set of 100 galaxies, showing that hybrid algorithm is very well suited to solve this problem.

关键词： learning algorithms

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the empirical study of applying Neuro to escalate marketing performance

The empirical study of applying Neuro to escalate marketing ...

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6th international conference on machine learning and Cybernetics

作者： Chen, Tong-Sheng Xiamen Univ Dept Comp Sci Xiamen 361005 Peoples R China

ISBN: (纸本)9781424409723

We develop a F-measure performance evaluation system by combining with RFM criteria and Bayesian classification, Decision Tree Induction, Gini Index, Neuro and by analyzing the factors with recall related and precision. By using this system, more profitable customers can be discovered and less unprofitable customers will be missed. the research collects and analyzes references on promoting buying rate;moreover, it introduces R-F-M (recency, frequency, monetary amount) criteria, brings up the idea of identify each individual customer to promote both the marketing profit and the customer's lifetime value. the result shows that marketing performance derive from Neuro-weighted RFM model has the advantage over traditional RFM model by 27.80%.

关键词： data mining Rfm Bayesian classification Decision Tree Induction Gini Neuro

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Proceedings of the 6th IEEE international conference on Cognitive Informatics, ICCI 2007

Proceedings of the 6th IEEE International Conference on Cogn...

引用

6th IEEE international conference on Cognitive Informatics, ICCI 2007

ISBN: (纸本)1424413273

the proceedings contain 71 papers. the topics discussed include: language understanding and unified cognitive science;cognitive informatics foundations of nature and machine intelligence;challenges in the design of adaptive, intelligent, and cognitive systems;a approach to representation changes while executing problem solver intelligent systems;formal linguistics and the deductive grammar;towards a spatial representation for the meta cognitive process layer of cognitive informatics;the visual implications of inspection time;image decomposition and reconstruction using two-dimensional complex-valued Gabor wavelets;cognitive informatics in automatic pattern understanding;a cognitive data visualization method based on hyper surface;and a simple high accuracy approach for face recognition.

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learning graph matching

Learning graph matching

引用

11th IEEE international conference on Computer Vision

作者： Caetano, Tiberio S. Cheng, Li Le, Quoc V. Smola, Alex J. NICTA Stat Machine Learning Program Canberra ACT 0200 Australia

ISBN: (纸本)9781424416301

As a fundamental problem in pattern recognition, graph matching has found a variety of applications in the field of computer vision. In graph matching, patterns are modeled as graphs and pattern recognition amounts to finding a correspondence between the nodes of different graphs. there are many ways in which the problem has been formulated, but most can be cast in general as a quadratic assignment problem, where a linear term in the objective function encodes node compatibility functions and a quadratic term encodes edge compatibility functions. the main research focus in this theme is about designing efficient algorithms for solving approximately the quadratic assignment problem, since it is NP-hard. In this paper, we turn our attention to the complementary problem: how to estimate compatibility functions such that the solution of the resulting graph matching problem best matches the expected solution that a human would manually provide. We present a method for learning graph matching: the training examples are pairs of graphs and the "labels" are matchings between pairs of graphs. We present experimental results with real image data which give evidence that learning can improve the performance of standard graph matching algorithms. In particular, it turns out that linear assignment with such a learning scheme may improve over state-of-the-art quadratic assignment relaxations. this finding suggests that for a range of problems where quadratic assignment was thought to be essential for securing good results, linear assignment, which is far more efficient, could be just sufficient if learning is performed. this enables speed-ups of graph matching by up to 4 orders of magnitude while retaining state-of-the-art accuracy.

关键词： Computer vision

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data clustering: User's dilemma 1

引用

5th international conference on machine learning and data mining in pattern recognition, MLDM 2007

作者： Jain, Anil K. Department of Computer Science and Engineering Michigan State University United States

ISBN: (数字)9783540734994

ISBN: (纸本)9783540734987

data clustering is a long standing research problem in pattern recognition, computer vision, machine learning, and data mining with applications in a number of diverse disciplines. the goal is to partition a set of n d-dimensional points into k clusters, where k may or may not be known. Most clustering techniques require the definition of a similarity measure between patterns, which is not easy to specify in the absence of any prior knowledge about cluster shapes. While a large number of clustering algorithms exist, there is no optimal algorithm. Each clustering algorithm imposes a specific structure on the data and has its own approach for estimating the number of clusters. No single algorithm can adequately handle various cluster shapes and structures that are encountered in practice. Instead of spending our effort in devising yet another clustering algorithm, there is a need to build upon the existing published techniques. In this talk we will address the following problems: (i) clustering via evidence accumulation, (ii) simultaneous clustering and dimensionality reduction, (iii) clustering under pair-wise constraints, and (iv) clustering with relevance feedback. Experimental results show that these approaches are promising in identifying arbitrary shaped clusters in multidimensional data. © Springer-Verlag Berlin Heidelberg 2007.

关键词： Clustering algorithms

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Reconstructing strokes and writing sequences from Chinese character images

Reconstructing strokes and writing sequences from Chinese ch...

引用

6th international conference on machine learning and Cybernetics

作者： Tang, Kai-Tai Leung, Howard City Univ Hong Kong Dept Comp Sci Kowloon Hong Kong Peoples R China

ISBN: (纸本)9781424409723

the Chinese characters evolved from pictograms and they are composed of strokes. A standard stroke sequence for each character is available in the dictionary. People introduced heuristic rules to specify the stroke order for easy memorization but it is very ambiguous to reconstruct the dictionary sequence according to the heuristic rules. In this paper, we combine the stroke extraction and stroke sequence reconstruction algorithms to reconstruct the strokes and their sequence from a Chinese character image. A well-known public Chinese character database (the HITPU database) is used as our input data. Performance evaluation shows the robustness of our proposed method and user evaluation shows that our proposed system helps users to create online Chinese character templates quickly and conveniently.

关键词： stroke sequence estimation stroke extraction pattern classification Chinese character HITPU database

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Hierarchical artificial neural networks for recognizing high similar large data sets

Hierarchical artificial neural networks for recognizing high...

引用

6th international conference on machine learning and Cybernetics

作者： Lu, Yen-Ling Fahn, Chin-Shyurng Natl Taiwan Univ Sci & Technol Dept Comp Sci & Informat Engn Taipei Taiwan

ISBN: (纸本)9781424409723

this paper proposes a hierarchical artificial neural network for recognizing high similar large data sets. It is usually required to classify large data sets with high similar characteristics in many applications. Analyzing and identifying those data is a laborious task when the methods adopted are primarily based on visual inspection. In many field applications, data sets are measured and recorded continuously using automatic monitoring equipments. therefore, a large amount of data can be collected, and manual inspection has become an unsuitable approach to recognizing those data. this proposed hierarchical neural network integrates self-organizing feature map (SOM) networks and learning vector quantization (LVQ) networks. the SOM networks provide an approximate method for computing the input vectors in an unsupervised manner. then the computation of the SOM may be viewed as the first stage of the proposed hierarchical network. the second stage is provided by the LVQ networks based on a supervised learning technique that uses class information to improve the quality of the classifier from the first stage. the multistage hierarchical network attempts to factorize the overall input vector into a number of small groups, each of which requires very little computation. Consequently, by use of the proposed network, the loss in accuracy can be small.

关键词： similarity search SOM LVQ recognition

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