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machine learning algorithms are widely used in classification problems. Certainly, recognition quality of algorithms is important indicator, but the ability of the algorithm to learn is more significant. In this work the learning curves experiment was performed in order to identify which of the three learning rates occur when training the machine learning algorithms: overfitting, perfect case and...
With the emerging increase of diabetes, that recently affects around 346 million people, of which more than one-third go undetected in early stage, a strong need for supporting the medical decision-making process is generated. A number of researches have focused either in using one of the algorithms or in the comparisons of the performances of algorithms on a given, usually predefined and static datasets...
Histopathological specimens are prepped through a process called staining prior to analysis by the pathologist. Staining of a pathological specimen is a standard procedure used to increase the contrast between the cell and tissue structures against the background. Unfortunately, staining is a lengthy process that requires hours of preparation. Moreover, the chemicals used to perform the procedure...
Astrology has started around 4000 years back and has significantly developed over a period of time. Till date no unified rules or standards for astrological prediction exist in the world. Astrologers concentrate on providing quality services to persons rather than defining universal rules and standards for astrological prediction. Advances in artificial intelligence resulted in large number of applications...
This paper presents an approach to morphological analysis of Malayalam words as a classification Problem. The idea here is to use Memory Based Language Processing (MBLP) algorithm for Malayalam morphological analysis. MBLP is an approach to language processing based on exemplar storage during learning, and analogical reasoning during processing. The aim of the system is to find the citation forms...
Network traffic classification plays an important role in the areas of network security, network monitoring, QoS and traffic engineering. In this paper, we design a network traffic classifier based on the statistical features extracted from network flows. Instead of deriving the statistical characteristics per flow, our model make use of features extracted from the first few seconds of each flows...
The wide application of Internet technology and media technology produces more and more data which also leads the arrival of the era of big data. However, it is difficult to extract the needed information from the original data directly except some special conditions. In recent years, the development of machine learning which provide a effective way to solve this problem for us. You can obtain lower...
Feature selection or variable reduction is a fundamental problem in data mining, refers to the process of identifying the few most important features for application of a learning algorithm. The best subset contains the minimum number of dimensions retaining a suitably high accuracy on classifier in representing the original features. The objective of the proposed approach is to reduce the number...
In silicon testing, a Shmoo plot is commonly used to give us an insight into the silicon manufacturing development health. Shmoo plots and other silicon characterization data has high value, however, analysis of them is a time-consuming work. This paper establishes a machine learning based model to improve and automate the procedure in silicon data analysis for HVM test content development. Our experiment...
Prototype selection aims at reducing the scale of datasets to improve prediction accuracy and operation efficiency by removing noisy or redundant patterns via the nearest neighbor classification algorithms. Genetic algorithms have been used recently for prototype selection and showed good performance, however, they have some drawbacks such as the deteriorated running effect, slow convergence for the...
Many algorithms are now available for doing the same task (e.g. binarization, page segmentation, character recognition, etc.) in document image analysis (DIA) and choosing a particular algorithm(s) for a particular task is often a non-trivial problem. This paper proposes a model for automatically selecting the correct algorithm(s) for a given problem. Binarization has been taken a reference to illustrate...
The work presented in this paper proposes a new approach of using subspace grids for recognizing patterns in multidimensional data. The proposed approach addresses the two problems often associated with this task: i) curse of dimensionality ii) cases with small sample sizes. To handle the curse of dimensionality problem, this paper introduces subspace grids and shows how it can be employed for pattern...
In this paper, we mainly propose an incremental version of improved least squares twin support vector machine (IILSTSVM), based on inverse matrix-free method. This algorithm can meet the requirement of online learning to update the existing model. In the case of low dimension data, this method effectively improves training speed of incremental learning. According to updating inverse matrix, we can...
Mining data streams has attracted the attention of the scientific community in recent years with the development of new algorithms for processing and sorting data in this area. Incremental learning techniques have been used extensively in these issues. A major challenge posed by data streams is that their underlying concepts can change over time. This research delves into the study of applying different...
In order to compare the classification accuracies and performance differences between traditional and probability-based decision tree classifiers, and come to understand those algorithms, which aim to improve construction efficiency of probability-based decision trees, mentioned in "Decisions Trees for Uncertain Data", this paper tested several algorithms, named AVG, UDT, UDT-BP, UDT-LP,...
This paper presents a novel and efficient decision tree construction approach based on C4.5. C4.S constructs decision tree with information gain ratio and deals with missing values or noise. ID3 and its improvement, C4.5, both select one attribute as the splitting criterion each time during constructing decision tree, adopting one step forward. Comparing with one step forward, the proposed algorithm,...
In the past, the concept of performing the task of feature selection by attribute clustering was proposed. Hong et al. thus proposed several genetic algorithms for finding appropriate attribute clusters. In this paper, we attempt to improve the performance of the GA-based attribute-clustering process based on the grouping genetic algorithm (GGA). In our approach, the general GGA representation and...
Within a learning machine, we could improve the accuracy of every learning task while a few tasks learned together. This method is called multitask learning. It is popular to using simulated annealing algorithm or setting a constant as learning rate in multitask learning. Either is simple and efficiently. But neither use feedback information in machine effectively. A novel adaptive feedback learning...
Feature selection has received considerable attentions in various areas as a way to select informative features and to simplify the statistical model through dimensional reduction. In this work, we introduce a novel concept, membership probability of a feature, and propose a novel approach to feature selection for clustering which can find the most optimal candidate features effectively among the...
Transfer learning, serving as one of the most important research directions in machine learning, has been studied in various fields in recent years. In this paper, we integrate the theory of multi-view learning into transfer learning and propose a new algorithm named Multi-View Transfer Learning with Adaboost (MV-TL Adaboost). Different from many previous works on transfer learning, we not only focus...
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