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195
port Vector Machines, Kernel Fisher Discriminant analysis
"... Abstract | This review provides an introduction to Sup- ..."
2005: Prediction of loblolly pine wood properties using transmittance nearinfrared spectroscopy
- Canadian Journal of Forest Research
"... Abstract: Near-infrared (NIR) spectroscopy is a rapid nondestructive technique that has been used to characterize chemical and physical properties of a wide range of materials. In this study, transmittance NIR spectra from thin wood wafers cut from increment cores were used to develop calibration mo ..."
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Cited by 6 (0 self)
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-cellulose content, respectively. Predicting ring 8 properties using ring 3 calibration equations showed potential for predicting α-cellulose content and fiber coarse-ness, with R2 values of approximately 0.60, indicating the potential for early selection. Predicting the wood properties using the calibration
FEATURE SELECTION FOR CLASSIFIER ACCURACY IMPROVEMENT
"... Abstract. Most of the time a lot of data means better results. This case is not valid all the time because sometimes we have a lot of redundant data and a lot of attributes that are weakly related to what we are trying to find out by evaluating the data. The main idea behind feature selection is to ..."
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and the time spent for classification. We have used the Support Vector Machine, a powerful classification technique based on kernels, which has proven to be efficient for nonlinearly separable input data. Basically what data mining tell us is that the more features we have, the better it is to make more
Application of a Feature Selection Method to Nucleosome Data: Accuracy Improvement and Comparison with Other Methods
- WSEAS Transactions on Biology and Biomedicine
"... Abstract:- In binary classification problem, data of feature vectors with binary labels are prepared in general. However, today it is well known that using all the features for discrimination does not always the best way to achieve the highest accuracy in prediction. Feature selection is a technique ..."
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Cited by 2 (2 self)
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technique to find a subset of features with the highest accuracy by eliminating features harmful in prediction. Among various methods proposed, in this study we used a method which can be divided in two steps. Firstly, along the ranked features f1,…,fn based on Gini index, the feature subsets {f1},{f1,f2
Workshop on Applications of Pattern Analysis Multiple Kernel Learning on the Limit Order Book
"... Simple features constructed from order book data for the EURUSD currency pair were used to construct a set of kernels. These kernels were used both individually and simultaneously through the Multiple Kernel Learning (MKL) methods of SimpleMKL and the more novel LPBoostMKL to train multiclass Suppor ..."
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Support Vector Machines to predict the direction of future price movements. The kernel methods outperformed a trend following benchmark both in their predictive ability and when used in a simple trading rule. Furthermore, the kernel weightings selected by the MKL techniques highlight which features
Approved as to style and content by:
, 2014
"... This Open Access Dissertation is brought to you for free and open access by the Dissertations and Theses at ScholarWorks@UMass Amherst. It has ..."
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This Open Access Dissertation is brought to you for free and open access by the Dissertations and Theses at ScholarWorks@UMass Amherst. It has
Web Page Structure Enhanced Feature Selection for Classification of Web Pages
, 2013
"... Web page classification is achieved using text classification techniques. Web page classification is different from traditional text classification due to additional information, provided by web page structure which provides much information on content importance. HTML tags provide visual web page r ..."
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for the exploitation of semantic-based feature selection is proposed to improve search and retrieval of web pages over large document repositories. The features are classified using Support Vector Machine (SVM) using different kernels. The experimental results show improved precision and recall with the proposed
forthcoming). Assessing Box Office Performance Using Movie Scripts: A Kernel-based Approach
- IEEE Transactions on Knowledge and Data Engineering
, 2014
"... Abstract—We develop a methodology to predict box office performance of a movie at the point of green-lighting, when only its script and estimated production budget are available. We extract three levels of textual features (genre and content, semantics, and bag-of-words) from scripts using screenwri ..."
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Cited by 1 (0 self)
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Abstract—We develop a methodology to predict box office performance of a movie at the point of green-lighting, when only its script and estimated production budget are available. We extract three levels of textual features (genre and content, semantics, and bag-of-words) from scripts using
www.iasir.net Improved Content based Texture Image Classification using Cascade RBF
"... Abstract: Content-based image retrieval (CBIR) systems aim to return the most relevant images in a database, according to the user’s opinion for a given query. Due to the dynamic nature of feature content of image of image user query are frequently changed and result of retrieval image are suffered. ..."
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we used RBF neural network function for better prediction of feature used in image retrieval. Our proposed method optimized the feature selection process and finally sends data to multiclass classifier for classification of data. Here we used support vector machine for multi-class classification
Fuzzy-rough sets assisted attribute selection
- IEEE TRANSACTIONS ON FUZZY SYSTEMS
, 2007
"... Attribute selection (AS) refers to the problem of selecting those input attributes or features that are most predictive of a given outcome; a problem encountered in many areas such as machine learning, pattern recognition and signal processing. Unlike other dimensionality reduction methods, attribu ..."
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Cited by 32 (7 self)
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Attribute selection (AS) refers to the problem of selecting those input attributes or features that are most predictive of a given outcome; a problem encountered in many areas such as machine learning, pattern recognition and signal processing. Unlike other dimensionality reduction methods
Results 1 - 10
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195
