the pattern model representation (PMR) of time series is proposed in this paper. PMR is based on a piecewise linear representation (PLR) and is effective at describing the tendency of time series. then, the pattern di...
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ISBN:
(纸本)1853128066
the pattern model representation (PMR) of time series is proposed in this paper. PMR is based on a piecewise linear representation (PLR) and is effective at describing the tendency of time series. then, the pattern distance can be calculated to measure the similarity of tendency. this method overcomes the problem of time series mismatch based on point distance. According to the numbers of series' segmentations, pattern distance has a multi-scale feature and can reflect different similarities with various bandwidths. Because normalization is unnecessary, the calculation consumption of pattern distance is low.
We propose a novel query-driven lazy teaming algorithm which attempts to discover useful local patterns, called support patterns, for classifying a given query. the teaming is customized to the query to avoid the hori...
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ISBN:
(纸本)0769521428
We propose a novel query-driven lazy teaming algorithm which attempts to discover useful local patterns, called support patterns, for classifying a given query. the teaming is customized to the query to avoid the horizon effect. We show that this query-driven teaming algorithm can guarantee to discover all support patterns with perfect expected accuracy in polynomial time. the experimental results on benchmark data sets also demonstrate that our teaming algorithm really has prominent learning performance.
this paper presents a general indexing framework in the purpose of multimedia databases(DBs) storage and mining. Indexing is usually used to accelerate the access to large multimedia DBs. the proposed frame aims at an...
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ISBN:
(纸本)0889864543
this paper presents a general indexing framework in the purpose of multimedia databases(DBs) storage and mining. Indexing is usually used to accelerate the access to large multimedia DBs. the proposed frame aims at an automatic learning strategy. In this way, a decision graph is built withthe help of symbolic and quantitative information. Experiments with 3D and 2D image DBs illustrate it.
In the past machinelearning algorithms have been successfully used in many problems, and are emerging as valuable data analysis tools. However, their serious practical use is affected by the fact, that more often tha...
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ISBN:
(纸本)0769521428
In the past machinelearning algorithms have been successfully used in many problems, and are emerging as valuable data analysis tools. However, their serious practical use is affected by the fact, that more often than not, they cannot produce reliable and unbiased assessments of their predictions' quality. In last years, several approaches for estimating reliability or confidence of individual classifiers have emerged, many of them building upon the algorithmic theory of randomness, such as (historically ordered) transduction-based confidence estimation, typicalness-based confidence estimation, and transductive reliability estimation. Unfortunately, they all have weaknesses: either they are tightly bound with particular learning algorithms, or the interpretation of reliability estimations is not always consistent with statistical confidence levels. In the paper we propose a joint approach that compensates the mentioned weaknesses by integrating typicalness-based confidence estimation and transductive reliability estimation into joint confidence machine. the resulting confidence machine produces confidence values in the statistical sense (e.g., a confidence level of 95% means that in 95% the predicted class is also a true class), as well as provides us with a general principle that is independent of to the particular underlying classifier We perform a series of tests with several different machinelearning algorithms in several problem domains. We compare our results withthat of a proprietary TCM-NN method as well as with kernel density estimation. We show that the proposed method significantly outperforms density estimation methods, and how it may be used to improve their performance.
Clustering is crucial to many applications in patternrecognition, datamining, and machinelearning. Evolutionary techniques have been used with success in clustering, but most suffer from several shortcomings. We fo...
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ISBN:
(纸本)3540223436
Clustering is crucial to many applications in patternrecognition, datamining, and machinelearning. Evolutionary techniques have been used with success in clustering, but most suffer from several shortcomings. We formulate requirements for efficient encoding, resistance to noise, and ability to discover the number of clusters automatically.
Instance independence is a critical assumption of traditional machinelearning methods contradicted by many relational datasets. For example, in scientific literature datasets there are dependencies among the referenc...
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ISBN:
(纸本)0769521428
Instance independence is a critical assumption of traditional machinelearning methods contradicted by many relational datasets. For example, in scientific literature datasets there are dependencies among the references of a paper Recent work on graphical models for relational data has demonstrated significant performance gains for models that exploit the dependencies among instances. In this paper we present relational dependency networks (RDNs), a new form of graphical model capable of reasoning with such dependencies in a relational setting. We describe the details of RDN models and outline their strengths, most notably the ability to learn and reason with cyclic relational dependencies. We present RDN models learned on a number of real-world datasets, and evaluate the models in a classification context, showing significant performance improvements. In addition, we use synthetic data to evaluate the quality of model learning and inference procedures.
the paper describes the "Rough Sets database System" (called in short the RSDS system) for the creation of bibliography on rough sets and their applications. this database is the most comprehensive online ro...
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ISBN:
(纸本)3540221174
the paper describes the "Rough Sets database System" (called in short the RSDS system) for the creation of bibliography on rough sets and their applications. this database is the most comprehensive online rough sets bibliography and accessible under the following web-site address: http://*** the service has been developed in order to facilitate the creation of rough sets bibliography, for various types of publications. At the moment the bibliography contains over 1400 entries from more than 450 authors. It is possible to create the bibliography in HTML or BibTeX format. In order to broaden the service contents it is possible to append new data using specially dedicated form. After appending data online the database is updated automatically. If one prefers sending a data file to the database administrator, please be aware that the database is updated once a month. In the current version of the RSDS system, there is the possibility for appending to each publication an abstract and keywords. As a natural consequence of this improvement there exists a possibility for searching a publication by keywords.
ILP systems have been largely applied to datamining classification tasks with a considerable success. the use of ILP systems in regression tasks has been far less successful. Current systems have very limited numerica...
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ISBN:
(纸本)0769521428
ILP systems have been largely applied to datamining classification tasks with a considerable success. the use of ILP systems in regression tasks has been far less successful. Current systems have very limited numerical reasoning capabilities, which limits the application of ILP to discovery of functional relationships of numeric nature. this paper proposes improvements in numerical reasoning capabilities of ILP systems for dealing with regression tasks. It proposes the use of statistical-based techniques like Model Validation and Model Selection to improve noise handling and it introduces a new search stopping criterium based on the PAC method to evaluate learning performance. We have found these extensions essential to improve on results over machinelearning and statistical-based algorithms used in the empirical evaluation study.
In this paper we present a new learning system, the "Intelligent learningmachine" (ELM). We associate intelligence withthe power to learn, forget, grow, contract, interact, and co-operate incrementally, on...
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ISBN:
(纸本)1853128066
In this paper we present a new learning system, the "Intelligent learningmachine" (ELM). We associate intelligence withthe power to learn, forget, grow, contract, interact, and co-operate incrementally, on-line, and in real time. Intelligence in the ILM is based upon the use of a specially customized weight table. the ILM enables parallel data processing and it is well suited to a wide variety of applications and promises unprecedented performance gains in dynamic environments. Here we show how Linear and Non-linear Regression and Classification modeling methods are transformed into intelligent methods. this method has now been successfully software implemented and tested using a variety of databases. Hardware implementation of the ILM is feasible and we foresee an ILM chip for faster computations and mobile applications. Subsequent papers will show how the ELM can be applied to methods such as Bayesian Models, Markov Chain, Hidden Markov Models, Linear Discriminant Analysis, Association Rules, OneR, Principal Component Analysis and Linear Support Vector machines.
the problem of classifying rarely occurring cases is faced in many real life applications. the scarcity of the rare cases makes it difficult to classify them correctly using traditional classifiers. In this paper, we ...
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ISBN:
(纸本)0769521428
the problem of classifying rarely occurring cases is faced in many real life applications. the scarcity of the rare cases makes it difficult to classify them correctly using traditional classifiers. In this paper, we propose a new approach to use emerging patterns (EPs) [3] and decision trees (DTs) in rare-class classification (EPDT). EPs are those itemsets whose supports in one class are significantly higher than their supports in the other classes. EPDT employs the power of EPs to improve the quality of rare-case classification. To achieve this aim, we first introduce the idea of generating new non-existing rare-class instances, and then we over-sample the most important rare-class instances. Our experiments show that EPDT outperforms many classification methods.
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