Support Vector machines have received considerable attention from the patternrecognition community in recent years. they have been applied to various classical recognition problems achieving comparable or even superi...
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ISBN:
(纸本)3540269231
Support Vector machines have received considerable attention from the patternrecognition community in recent years. they have been applied to various classical recognition problems achieving comparable or even superior results to classifiers such as neural networks. We investigate the application of Support Vector machines (SVMs) to the problem of road recognition from remotely sensed images using edge-based features. We present very encouraging results from our experiments, which are comparable to decision tree and neural network classifiers.
this paper proposes an unsupervised algorithm for learning a finite Dirichlet mixture model. An important part of the unsupervised learning problem is determiningthe number of clusters which best describe the data. W...
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ISBN:
(纸本)3540269231
this paper proposes an unsupervised algorithm for learning a finite Dirichlet mixture model. An important part of the unsupervised learning problem is determiningthe number of clusters which best describe the data. We consider here the application of the Minimum Message length (MML) principle to determine the number of clusters. the Model is compared with results obtained by other selection criteria (AIC, MDL, MMDL, PC and a Bayesian method). the proposed method is validated by synthetic data and summarization of texture image database.
this article addresses the task of mining concepts from biomedical literature to index and search through this documents base. this research takes place within the Telemakus project, which has for goal to support and ...
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ISBN:
(纸本)3540269231
this article addresses the task of mining concepts from biomedical literature to index and search through this documents base. this research takes place within the Telemakus project, which has for goal to support and facilitate the knowledge discovery process by providing retrieval, visual, and interaction tools to mine and map research findings from research literature in the field of aging. A concept mining component automating research findings extraction such as the one presented here, would permit Telemakus to be efficiently applied to other domains. the main principle that has been followed in this project has been to mine from the legends of the documents the research findings as relationships between concepts from the medical literature. the concept mining proceeds through stages of syntactic analysis, semantic analysis, relationships building, and ranking.
We present how the supervised machinelearning techniques can be used to predict quality characteristics in an important chemical engineering application: the wine distillate maturation process. A number of experiment...
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ISBN:
(纸本)3540269231
We present how the supervised machinelearning techniques can be used to predict quality characteristics in an important chemical engineering application: the wine distillate maturation process. A number of experiments have been conducted with six regression-based algorithms, where the M5' algorithm was proved to be the most appropriate for predicting the organoleptic properties of the matured wine distillates. the rules that are exported by the algorithm are as accurate as human expert's decisions.
the scientific community has accumulated an immense experience in processing data represented in finite-dimensional linear spaces of numerical features of entities, whereas the kit of mathematical instruments for diss...
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ISBN:
(纸本)3540269231
the scientific community has accumulated an immense experience in processing data represented in finite-dimensional linear spaces of numerical features of entities, whereas the kit of mathematical instruments for dissimilarity-based processing of data in metric spaces representing distances between entities, for which sufficiently informative features cannot be found, is much poorer. In this work, the problem of embedding the given set of entities into a linear space with inner product by choosing an appropriate kernel function is considered as the major challenge in the featureless approach to estimating dependences in data sets of arbitrary kind. As a rule, several kernels may be heuristically suggested within the bounds of the same data analysis problem. We treat several kernels on a set of entities as Cartesian product of the respective number of linear spaces, each supplied with a specific kernel function as a specific inner product. the main requirement here is. to avoid discrete selection in eliminating redundant kernels withthe purpose of achieving acceptable computational complexity of the fusion algorithm.
During the last years, computer vision tasks like object recognition and localization were rapidly expanded from passive solution approaches to active ones, that is to execute a viewpoint selection algorithm in order ...
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ISBN:
(纸本)3540269231
During the last years, computer vision tasks like object recognition and localization were rapidly expanded from passive solution approaches to active ones, that is to execute a viewpoint selection algorithm in order to acquire just the most significant views of an arbitrary object. Although fusion of multiple views can already be done reliably, planning is still limited to gathering the next best view, normally the one providing the highest immediate gain in information. In this paper, we show how to perform a generally more intelligent, long-run optimized sequence of actions by linking them with costs. therefore it will be introduced how to acquire the cost of an appropriate dimensionality in a non-empirical way while still leaving the determination of the system's basic behavior to the user. Since this planning process is accomplished by an underlying machinelearning technique, we also point out the ease of adjusting these to the expanded task and show why to use a multi-step approach for doing so.
this paper is concerned with time series of graphs and proposes a novel scheme that is able to predict the presence or absence of nodes in a graph. the proposed scheme is based on decision trees that are induced from ...
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ISBN:
(纸本)3540269231
this paper is concerned with time series of graphs and proposes a novel scheme that is able to predict the presence or absence of nodes in a graph. the proposed scheme is based on decision trees that are induced from a training set of sample graphs. the work is motivated by applications in computer network monitoring. However, the proposed prediction method is generic and can be used in other applications as well. Experimental results with graphs derived from real computer networks indicate that a correct prediction rate of up to 97% can be achieved.
In this paper we describe a new cluster model which is based on the concept of linear manifolds. the method identifies subsets of the data which are embedded in arbitrary oriented lower dimensional linear manifolds. M...
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ISBN:
(纸本)3540269231
In this paper we describe a new cluster model which is based on the concept of linear manifolds. the method identifies subsets of the data which are embedded in arbitrary oriented lower dimensional linear manifolds. Minimal subsets of points are repeatedly sampled to construct trial linear manifolds of various dimensions. Histograms of the distances of the points to each trial manifold are computed. the sampling corresponding to the histogram having the best separation between a mode near zero and the rest is selected and the data points are partitioned on the basis of the best separation. the repeated sampling then continues recursively on each block of the partitioned data. A broad evaluation of some hundred experiments over real and synthetic data sets demonstrates the general superiority of this algorithm over any of the competing algorithms in terms of stability, accuracy, and computation time.
We propose an approach to embed time series data in a vector space based on the distances obtained from Dynamic Time Warping (DTW), and to classify them in the embedded space. Under the problem setting in which both l...
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ISBN:
(纸本)3540269231
We propose an approach to embed time series data in a vector space based on the distances obtained from Dynamic Time Warping (DTW), and to classify them in the embedded space. Under the problem setting in which both labeled data and unlabeled data are given beforehand, we consider three embeddings, embedding in a Euclidean space by MDS, embedding in a Pseudo-Euclidean space, and embedding in a Euclidean space by the Laplacian eigenmap technique. We have found through analysis and experiment that the embedding by the Laplacian eigemnap method leads to the best classification result. Furthermore, the proposed approach with Laplacian eigenmap embedding shows better performance than k-nearest neighbor method.
An integrated approach of mining association rules and meta-rules based on a hyper-structure is put forward. In this approach, time serial databases are partitioned according to time segments, and the total number of ...
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ISBN:
(纸本)3540269231
An integrated approach of mining association rules and meta-rules based on a hyper-structure is put forward. In this approach, time serial databases are partitioned according to time segments, and the total number of scanning database is only twice. In the first time, a set of 1-frequent itemsets and its projection database are formed at every partition. then every projected database is scanned to construct a hyper-structure. through miningthe hyper-structure, various rules, for example, global association rules, meta-rules, stable association rules and trend rules etc. can be obtained. Compared with existing algorithms for mining association rule, our approach can mine and obtain more useful rules. Compared with existing algorithms for meta-mining or change mining, our approach has higher efficiency. the experimental results show that our approach is very promising.
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