this book constitutes the refereed proceedings of the 4thinternationalconference on patternrecognition and machine Intelligence, PReMI 2011, held in Moscow, Russia in June/July 2011. the 65 revised papers presented...
ISBN:
(数字)9783642217869
ISBN:
(纸本)9783642217852
this book constitutes the refereed proceedings of the 4thinternationalconference on patternrecognition and machine Intelligence, PReMI 2011, held in Moscow, Russia in June/July 2011. the 65 revised papers presented together with 5 invited talks were carefully reviewed and selected from 140 submissions. the papers are organized in topical sections on patternrecognition and machinelearning; image analysis; image and video information retrieval; natural language processing and text and datamining; watermarking, steganography and biometrics; soft computing and applications; clustering and network analysis; bio and chemo analysis; and document image processing.
Education is acknowledged to be the primary vehicle for improving the economic well-being of people [1,6]. Textbooks have a direct bearing on the quality of education imparted to the students as they are the primary c...
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ISBN:
(纸本)9783642217869
Education is acknowledged to be the primary vehicle for improving the economic well-being of people [1,6]. Textbooks have a direct bearing on the quality of education imparted to the students as they are the primary conduits for delivering content knowledge [9]. they are also indispensable for fostering teacher learning and constitute a key component of the ongoing professional development of the teachers [5,8].
Nowadays, classification is one of the many fields in datamining, also known as Knowledge Discovery in databases, which aims at extracting information from large data volumes. In order to achieve this, datamining us...
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In this paper we propose a novel data clustering algorithm based on the idea of considering the individual data items as cells belonging to an uni-dimensional cellular automaton. Our proposed algorithm combines insigh...
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ISBN:
(纸本)9783642213441
In this paper we propose a novel data clustering algorithm based on the idea of considering the individual data items as cells belonging to an uni-dimensional cellular automaton. Our proposed algorithm combines insights from both social segregation models based on Cellular Automata theory, where the data items themselves are able to move autonomously in lattices, and also from Ants Clustering algorithms, particularly in the idea of distributing at random the data items to be clustered in lattices. We present a series of experiments with both synthetic and real datasets in order to study empirically the convergence and performance results. these experimental results are compared to the obtained by conventional clustering algorithms.
the proceedings contain 15 papers. the topics discussed include: estimating probability of failure of a complex system based on partial information about subsystems and components, with potential applications to aircr...
the proceedings contain 15 papers. the topics discussed include: estimating probability of failure of a complex system based on partial information about subsystems and components, with potential applications to aircraft maintenance;stepwise feature selection using multiple kernel learning;empirical reconstruction of fuzzy model of experiment in the Euclidean metric;SVM based offline handwritten gurmukhi character recognition;obtaining of a minimal polygonal representation of a curve by means of a fuzzy clustering;KDDClus: a simple method for multi-density clustering;intelligent datamining for turbo-generator predictive maintenance: an approach in real-world;handwritten script identification from a bi-script document at line level using gabor filters;image recognition using kullback-leibler information discrimination;beyond analytical modeling, gathering data to predict real agents' strategic interaction;and construction of enzyme network of arabidopsis thaliana using graph theory.
Nowadays computer scientists are faced with fast growing and permanently evolving data, which are represented as observations made sequentially in time. A common problem in the datamining community is the recognition...
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Nowadays, classification is one of the many fields in datamining, also known as Knowledge Discovery in databases, which aims at extracting information from large data volumes. In order to achieve this, datamining us...
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Nowadays, classification is one of the many fields in datamining, also known as Knowledge Discovery in databases, which aims at extracting information from large data volumes. In order to achieve this, datamining uses different computational techniques from machinelearning, statistics and patternrecognition. In this work, a datamining techniques is used to help the Decision Maker of a Marin Transportation Firm called “SONOTRAK” to allocate a ship for a trip. the target is to ensure the transportation between “Sfax” city (Tunisia) and a closed island called “Kerkennah”. the fleet of “SONOTRAK” consists of five ships with different passenger and cars capabilities. the obtained classification gives groups of similar trips. Each class will be subject to arrange available ships according to the history of allocated trips in the last year. the most used ship will be the preferred one, and so on. Each ship will have a fitness value calculated according to this arrangement and to its fuel cost. the ship withthe better fitness value will be allocated to the trip. the result ensures better management of the fleet of the company, and gives effect not only on the overall traffic but also on the fuel costs.
In this paper we present a sequential expectation maximization algorithm to adapt in an unsupervised manner a Gaussian mixture model for a classification problem. the goal is to adapt the Gaussian mixture model to cop...
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the goal of statistical pattern feature extraction (SPFE) is 'low loss dimension reduction'. As the key link of patternrecognition, dimension reduction has become the research hot spot and difficulty in the f...
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ISBN:
(纸本)9783540859833
the goal of statistical pattern feature extraction (SPFE) is 'low loss dimension reduction'. As the key link of patternrecognition, dimension reduction has become the research hot spot and difficulty in the fields of patternrecognition, machinelearning, datamining and so on. pattern feature extraction is one of the most challenging research fields and has attracted the attention from many scholars. this paper summarily introduces the basic principle of SPFE, and discusses the latest progress of SPFE from the aspects such as classical statistical theories and their modifications, kernel-based methods, wavelet analysis and its modifications, algorithms integration and so on. At last we discuss the development trend of SPFE.
Predictive Toxicology (PT) is one of the newest targets of the Knowledge Discovery in databases (KDD) domain. Its goal is to describe the relationships between the chemical structure of chemical compounds and biologic...
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Predictive Toxicology (PT) is one of the newest targets of the Knowledge Discovery in databases (KDD) domain. Its goal is to describe the relationships between the chemical structure of chemical compounds and biological and toxicological processes. In real PT problems there is a very important topic to be considered: the huge number of the chemical descriptors. Irrelevant, redundant, noisy and unreliable data have a negative impact, therefore one of the main goals in KDD is to detect these undesirable proprieties and to eliminate or correct them. this assumes data cleaning, noise reduction and feature selection because the performance of the applied machinelearning algorithms is strongly related withthe quality of the data used. In this paper, we present some of the issues that can be taken into account for preparing data before the actual knowledge discovery is performed.
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