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Stock trend prediction is regarded as one of the most challenging tasks of financial time series prediction. Conventional statistical modeling techniques are not adequate for stock trend forecasting because of the non-stationarity and non-linearity of the stock market. With this regard, many machine learning approaches are used to improve the prediction results. These approaches mainly focus on two aspects: regression problem of the stock price and prediction problem of the turning points of stock price. In this paper, we concentrate on the evaluation of the current trend of stock price and the prediction of the change orientation of the stock price in future. Then, a new approach named status box method is proposed. Different from the prediction issue of the turning points, the status box method packages some stock points into three categories of boxes which indicate different stock status. And then, some machine learning techniques are used to classify these boxes so as to measure whether the states of each box coincides with the stock price trend and forecast the stock price trend based on the states of the box. These results would support us to make buying or selling strategies. Comparing with the turning points prediction that only considered the features of one day, each status box contains a certain amount of points which represent the stock price trend in a certain period of time. So, the status box reflects more information of stock market. To solve the classification problem of the status box, a special features construction approach is presented. Moreover, a new ensemble method integrated with the AdaBoost algorithm, probabilistic support vector machine (PSVM), and genetic algorithm (GA) is constructed to perform the status boxes classification. To verify the applicability and superiority of the proposed methods, 20 shares chosen from Shenzhen Stock Exchange (SZSE) and 16 shares from National Association of Securities Dealers Automated Quotations (NASDAQ) are applied to perform stock trend prediction. The results show that the status box method not only have the better classification accuracy but also effectively solve the unbalance problem of the stock turning points classification. In addition, the new ensemble classifier achieves preferable profitability in simulation of stock investment and remarkably improves the classification performance compared with the approach that only uses the PSVM or back-propagation artificial neural network (BPN).  相似文献   
2.
A better similarity index structure for high-dimensional feature datapoints is very desirable for building scalable content-based search systems on feature-rich dataset. In this paper, we introduce sparse principal component analysis (Sparse PCA) and Boosting Similarity Sensitive Hashing (Boosting SSC) into traditional spectral hashing for both effective and data-aware binary coding for real data. We call this Sparse Spectral Hashing (SSH). SSH formulates the problem of binary coding as a thresholding a subset of eigenvectors of the Laplacian graph by constraining the number of nonzero features. The convex relaxation and eigenfunction learning are conducted in SSH to make the coding globally optimal and effective to datapoints outside the training data. The comparisons in terms of F1 score and AUC show that SSH outperforms other methods substantially over both image and text datasets.  相似文献   
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In this paper, we propose new methods for palmprint classification and handwritten numeral recognition by using the contourlet features. The contourlet transform is a new two dimensional extension of the wavelet transform using multiscale and directional filter banks. It can effectively capture smooth contours that are the dominant features in palmprint images and handwritten numeral images. AdaBoost is used as a classifier in the experiments. Experimental results show that the contourlet features are very stable features for invariant palmprint classification and handwritten numeral recognition, and better classification rates are reported when compared with other existing classification methods.  相似文献   
5.
An Adaptive Version of the Boost by Majority Algorithm   总被引:6,自引:0,他引:6  
Freund  Yoav 《Machine Learning》2001,43(3):293-318
We propose a new boosting algorithm. This boosting algorithm is an adaptive version of the boost by majority algorithm and combines bounded goals of the boost by majority algorithm with the adaptivity of AdaBoost.The method used for making boost-by-majority adaptive is to consider the limit in which each of the boosting iterations makes an infinitesimally small contribution to the process as a whole. This limit can be modeled using the differential equations that govern Brownian motion. The new boosting algorithm, named BrownBoost, is based on finding solutions to these differential equations.The paper describes two methods for finding approximate solutions to the differential equations. The first is a method that results in a provably polynomial time algorithm. The second method, based on the Newton-Raphson minimization procedure, is much more efficient in practice but is not known to be polynomial.  相似文献   
6.
为了准确计算煤矿的产量,需要把煤矸石的量减掉,针对这个问题,研究了基于图像识别的煤矸石识别技术,从煤矸石与煤炭的样本数据中分离数据,最终完成煤矸石的识别系统。采用自适应增强算法( AdaBoost 算法)对实现目标的检测达到了很好的效果,虽然原煤图像存在着多样性,受到遮挡、光照、视角等的影响,通过Ada-Boost算法对原煤数据库和非原煤数据库训练逐步提升原煤分类器性能,能成功实现原煤识别检测。论文中识别系统充分利用图像识别技术和人工智能思想,将机器学习引入煤矸石模型的建模环节,成功实现煤炭和煤矸石的区分。  相似文献   
7.
Driver fatigue severely affects driver's alertness and ability to drive safely. There are vital problems related to drivers fatigue on driving of trains, vehicles and airplanes. Therefore, the driver fatigue research is important. In this paper, we first study the impact of eye locations on face recognition accuracy, with Haar-like feature and AdaBoost classifier, face and eye area can be detected quickly and accurately. In the part of eye tracking, cam-shift based mean-shift algorithm is used to track the eyes. This method could automatically adjust the size of tracking window according to the different posture of driver. The performance of our eye detection method is validated by using image database with more than 6000 pictures. In addition, our real-time eye tracking system has been tested on railway line segment (China). There are 5 train drivers involved in the experiment. The validation shows that our eye detector has an overall 93% eye detection rate.  相似文献   
8.
本文提出利用Gentle AdaBoost (GAB) 训练一个层叠结构的目标检测器,然后基于训练出的检测器结构引入两种策略,设计了5种应用于后续粒子滤波跟踪的似然函数.为估计目标出现的概率,提出了两种构造似然函数的策略:层内概率统计(PIS)策略和层间概率统计(POS)策略.PIS表示在同一层内每个弱分类器的实数输出的概率统计,POS为实现层叠检测器在检测时所到达深度的概率统计.基于这两种策略,设计出了5种似然函数的形式:基于层叠结构的层内概率密度估计似然函数(PIS-CA)、基于合成结构的层内概率密度估计似然函数(PIS-EA)、层间概率密度估计似然函数(POS)、顺序组合层叠检测器的层内概率密度估计似然函数(S-PIS-POS)和逆序组合层叠检测器的似然函数(A-PIS-POS).实验表明,所定义的似然函数可以很好地估计目标出现的概率,在目标出现的区域比背景区域具有更大的置信度,整合PIS和POS两种策略的似然函数具备最优的性能.  相似文献   
9.
介绍中文文本分类的流程及相关技术。在分析传统的文本特征选择不足的基础上,提出了基于粗糙集与集成学习结合的文本分类方法,通过粗糙集进行文本的特征选择,采用一种集成学习算法AdaBoost.M1来提高弱分类器的分类性能,对中文文本进行分类。实验证明,这种算法分类结果的F1值比C4.5、kNN分类器都高,具有更加优良的分类性能。  相似文献   
10.
针对常用字符识别速度和精度矛盾的问题,提出了改进的AdaBoost字符识别算法。利用先验知识的稳定特征将字符集进行完全二分类,在此基础上分别训练级联的分类器,在充分的样本学习后可得到较高的识别正确率。针对AdaBoost算法的计算量大,用纯软件实现难以满足工业应用的实时性要求,根据其大量的乘累加运算相似性,基于积分图像和FPGA的并行结构来快速实现。实验结果表明,该算法能够满足印刷质量在线检测系统的识别正确率和实时性要求。  相似文献   
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