Robust predictive model for evaluating breast cancer survivability
Objective Many machine learning models have aided medical specialists in diagnosis and
prognosis for breast cancer. Accuracy has been regarded as a primary measurement for the …
prognosis for breast cancer. Accuracy has been regarded as a primary measurement for the …
Breast cancer survivability prediction using labeled, unlabeled, and pseudo-labeled patient data
Background Prognostic studies of breast cancer survivability have been aided by machine
learning algorithms, which can predict the survival of a particular patient based on historical …
learning algorithms, which can predict the survival of a particular patient based on historical …
Prediction of movement direction in crude oil prices based on semi-supervised learning
Oil price prediction has long been an important determinant in the management of most
sectors of industry across the world, and has therefore consistently required detailed …
sectors of industry across the world, and has therefore consistently required detailed …
Stock price prediction based on a complex interrelation network of economic factors
Stock price prediction is a field that has been continuously interesting. Stock prices are
influenced by many factors such as oil prices, exchange rates, money interest rates, stock …
influenced by many factors such as oil prices, exchange rates, money interest rates, stock …
Bioinformatics: Trends in gene expression analysis
SA Raut, SR Sathe, A Raut - 2010 International Conference on …, 2010 - ieeexplore.ieee.org
Bioinformatics is an interesting combination of biology and computational sciences, which
help scientists and researchers to do more biological experiments to improve the life of living …
help scientists and researchers to do more biological experiments to improve the life of living …
Active learning using Fuzzy-Rough Nearest Neighbor classifier for cancer prediction from microarray gene expression data
Cancer prediction from gene expression data is a very challenging area of research in the
field of computational biology and bioinformatics. Conventional classifiers are often unable …
field of computational biology and bioinformatics. Conventional classifiers are often unable …
Electricity price prediction based on semi-supervised learning and neural network algorithms
HS Kim, HJ Shin - Journal of Korean Institute of Industrial …, 2013 - koreascience.kr
Predicting monthly electricity price has been a significant factor of decision-making for plant
resource management, fuel purchase plan, plans to plant, operating plan budget, and so on …
resource management, fuel purchase plan, plans to plant, operating plan budget, and so on …
基于两阶段学习的半监督支持向量机分类算法
陶新民, 曹盼东, 宋少宇, 付丹丹 - 信息与控制, 2012 - xk.sia.cn
提出了一种基于两阶段学习的半监督支持向量机(semi-supervised SVM) 分类算法.
首先使用基于图的标签传递算法给未标识样本赋予初始伪标识, 并利用k 近邻图将可能的噪声 …
首先使用基于图的标签传递算法给未标识样本赋予初始伪标识, 并利用k 近邻图将可能的噪声 …
[图书][B] Algorithms for training large-scale linear programming support vector regression and classification
PR Perea - 2011 - search.proquest.com
The main contribution of this dissertation is the development of a method to train a Support
Vector Regression (SVR) model for the large-scale case where the number of training …
Vector Regression (SVR) model for the large-scale case where the number of training …
Uni-class pattern-based classification model
MA Salama, AE Hassanien… - 2010 10th International …, 2010 - ieeexplore.ieee.org
This paper presents a model of a supervised machine learning approach for classification of
a dataset. The model extracts a set of patterns common in a single class from the training …
a dataset. The model extracts a set of patterns common in a single class from the training …