this paper presents a novel method to predict the trends of topics on Twitter based on MACD (Moving Average Convergence-Divergence),which is one of the simplest and most effective momentum indicator in technique analy...
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this paper presents a novel method to predict the trends of topics on Twitter based on MACD (Moving Average Convergence-Divergence),which is one of the simplest and most effective momentum indicator in technique analysis of *** MACD turns two trend-following indicators,moving averages,into a momentum oscillator by subtracting the longer moving average from the shorter moving *** a result,we monitor the key words of topics on Twitter,and use the longer moving average and the shorter moving average to track their longer and shorter trends,***,we redefine the trends momentum with two moving averages according to the developmental characteristics of topics on Twitter,and use it to predict the *** results show that the proposed method is very simple and effective.
Based on the features such as high convergence rate and global optimization of Support Vector machine (SVM) which follows structure risk minimization principle, a method of fire detection is proposed, in which the sha...
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Based on the features such as high convergence rate and global optimization of Support Vector machine (SVM) which follows structure risk minimization principle, a method of fire detection is proposed, in which the shape of bright areas are analyzed by SVM and results are produced. After collecting images of fire and interference source under different conditions, datas of shape features are extracted. Many of them are used as training set and delivered to SVM;the other data are used as test set for patternrecognition. Fire experiments show that trained SVM with RBF kernel and SMO algorithm can recognize images with 94.32% accuracy rate.
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 data mining; watermarking, steganography and biometrics; soft computing and applications; clustering and network analysis; bio and chemo analysis; and document image processing.
the overview presents the development and application of Hierarchical Temporal Memory (HTM). HTM is a new machinelearning method which was proposed by Jeff Hawkins in 2005. It is a biologically inspired cognitive met...
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the overview presents the development and application of Hierarchical Temporal Memory (HTM). HTM is a new machinelearning method which was proposed by Jeff Hawkins in 2005. It is a biologically inspired cognitive method based on the principle of how human brain works. the method invites hierarchical structure and proposes a memory-prediction framework, thus making it able to predict what will happen in the near future. this overview mainly introduces the developing process of HTM, as well as its principle, characteristics, advantages and applications in vision, image processing and robots movement, some potential applications by using HTM , such as thinking process, are also put forward.
the problem of reconstructing dependencies from empirical data became very important in a very large range of applications. Procedures used to solve this problem are known as “Methods of machinelearning&...
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ISBN:
(纸本)9783642217869
the problem of reconstructing dependencies from empirical data became very important in a very large range of applications. Procedures used to solve this problem are known as “Methods of machinelearning” [1,3]. these procedures include methods of regression reconstruction, inverse problems of mathematical physics and statistics, machinelearning in patternrecognition (for visual and abstract patterns represented by sets of features) and many others. Many web network control problems also belong to this field. the task is to reconstruct the dependency between input and output data as precisely as possible using empirical data obtained from experiments or statistical observations.
In this paper we consider the problem of structured document recognition. the document recognition system is proposed. this system incorporates a recognition module based on methods of structured image recognition, a ...
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ISBN:
(纸本)9783642217869
In this paper we consider the problem of structured document recognition. the document recognition system is proposed. this system incorporates a recognition module based on methods of structured image recognition, a graph document model and a method of document model generalization. the machinelearning component makes the process of document model construction easier and less time-consuming.
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].
the classical learning problem of the patternrecognition in a finite-dimensional linear space of real-valued features is studied under the conditions of a non-stationary universe. the training criterion of non-statio...
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ISBN:
(纸本)9783642217869
the classical learning problem of the patternrecognition in a finite-dimensional linear space of real-valued features is studied under the conditions of a non-stationary universe. the training criterion of non-stationary patternrecognition is formulated as a generalization of the classical Support Vector machine. the respective numerical algorithm has the computation complexity proportional to the length of the training time series.
In this paper, we consider the problem of extracting opinions from natural language texts, which is one of the tasks of sentiment analysis. We provide an overview of existing approaches to sentiment analysis including...
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ISBN:
(纸本)9783642217869
In this paper, we consider the problem of extracting opinions from natural language texts, which is one of the tasks of sentiment analysis. We provide an overview of existing approaches to sentiment analysis including supervised (Naive Bayes, maximum entropy, and SVM) and unsupervised machinelearning methods. We apply three supervised learning methods-Naive Bayes, KNN, and a method based on the Jaccard index - to the dataset of Internet user reviews about cars and report the results. When learning a user opinion on a specific feature of a car such as speed or comfort, it turns out that training on full unprocessed reviews decreases the classification accuracy. We experiment with different approaches to preprocessing reviews in order to obtain representations that are relevant for the feature one wants to learn and show the effect of each representation on the accuracy of classification.
We propose a combinatorial technique for obtaining tight data dependent generalization bounds based on a splitting and connectivity graph (SC-graph) of the set of classifiers. We apply this approach to a parametric se...
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ISBN:
(纸本)9783642217869
We propose a combinatorial technique for obtaining tight data dependent generalization bounds based on a splitting and connectivity graph (SC-graph) of the set of classifiers. We apply this approach to a parametric set of conjunctive rules and propose an algorithm for effective SC-bound computation. Experiments on 6 data sets from the UCI ML Repository show that SC-bound helps to learn more reliable rule-based classifiers as compositions of less overfitted rules.
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