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The following topics are dealt with: machine learning; swarm intelligence; constraint programming; evolutionary algorithms; data mining; uncertainty handling; natural language processing; image processing; robotics; and multiagent systems.
To relieve "News Information Overload", classification, summarization and recommendation techniques have been proposed. However, these techniques fail to provide sufficient semantic information about news events. In this paper, considering5W1H (Who, What, Whom, When, Where and How), the full list of elements of a news article, we propose a novel approach to extract event semantic elements...
Nonnegative Matrix Factorization (NMF) has been widely used in dimensionality reduction, machine learning, and data mining, etc. It aims to find two nonnegative matrices whose product can well approximate the nonnegative data matrix, which naturally lead to parts-based representation. In this paper, we present a family of projective nonnegative matrix factorization algorithm, PNMF with Bregman divergence...
Data mining of network data often focuses on classification methods from machine learning, statistics, and pattern recognition perspectives. These techniques have been described by many, but many of these researchers are unaware of the rich history of classification and clustering techniques originating in social network analysis. The growth of rich social media, on-line communities, and collectively...
In recent years high-resolution space borne images have disclosed a large number of new opportunities for medium and large-scale rubber plant mapping. Some traditional algorithms used for hyper spectral remote sensing image classification have some problems such as low computing rate, low accuracy. According to SVM theory, the Rubber plant classification model based on SVM was constructed, by experimenting...
Medical data mining is so challenging. In this paper, we propose a new data mining algorithm called GAJA2, which is a derivation of GAJA [1]. We apply GAJA2 to mine Acute Inflammations data set, a medical data set got from UCI machine learning repository 2009[2]. This data set is about symptoms and diagnosis of two diseases of urinary system which are inflammation of urinary bladder and Nephritis...
Information on Internet is so huge,and increases quickly. A huge information pool is coming up on the Internet. And much important information can be found on the Internet. Internet is really an important source of information. So extract the key words from the pages on Internet is so important. A page description model based on undirection graph is proposed and then a entity ranking algorithm named...
Q-learning is a machine learning technique that learns what to do and how to map states to actions to maximize rewards. Q-learning has been applied to various tasks such as foraging, soccer and prey-pursuing robots. In this paper, a simple foraging task has been considered to study the influences of the policies reported in the open literatures. A mobile robot is used to search and retrieve pucks...
The introduction of multi-core architectures generates a higher demand for parallelism in order to fully exploit the potential of modern computers. It is of vital importance that a compiler can allocate parallel workload in a cost-aware manner in order to achieve optimal performance on a multi-core architecture. This paper presents an adaptive OpenMP-based mechanism capable of generating a reasonable...
We consider the problem of incremental learning of context-free grammars, using inductive CYK (Cocke-Younger-Kasami) algorithm, based on the non-deterministic learning scheme proposed by Nakamura and Matsumoto in 2005. We implement their learning scheme deterministically and illustrate several examples in order to understand the incremental learning process efficiently. On the basis of this study...
Specification mining is a machine learning approach for discovering specifications of the protocols that code must obey when interacting with an application program interface or abstract data type. Two major concerns in engineering software systems are high maintenance costs and reliability of systems. To reduce maintenance efforts, there is a need for automated tools to help software developers understand...
The paper proposes a method for the classification of EEG signal based on machine learning methods. We analyzed the data from an EEG experiment consisting of affective picture stimuli presentation, and tested automatic recognition of the individual emotional states from the EEG signal using Bayes classifier. The mean accuracy was about 75 percent, but we were not able to select universal features...
One of the crucial tasks in many inference problems is the extraction of an underlying sparse graphical model from a given number of high-dimensional measurements. In machine learning, this is frequently achieved using, as a penalty term, the Lp norm of the model parameters, with p ?? 1 for efficient dilution. Here we propose a statistical-mechanics analysis of the problem in the setting of perceptron...
In MIMO-OFDM systems, by matching transmitter parameters such as modulation order and coding rate, link adaptation can increase the throughput significantly. However, creating a tractable mathematical mapping model from environmental variables to transmitter parameters that allows the latter to be optimized in any sense, presents serious challenges due to the large number of variables involved, as...
Stream ciphers are widely used for information security. The keystream produced by a cipher must be unpredictable. Attacks on stream ciphers typically exploit some underlying patterns existing in the keystream. The objective of this paper is to develop such an attack with the help of machine learning algorithms. The Linear Feedback Shift Register (LFSR) has been solved for several test cases using...
A method to classify single ion channel is presented. The classes are built up by the K-means method and a generalized entropy based classifier is herein presented.
Enterprise performance evaluation is an important means of enterprise management, which can diagnose the whole development status of enterprise. Data envelopment analysis (DEA) is one of the most frequently used evaluation methods and support vector machine (SVM) is a novel method of data mining, which can be used for prediction and regression. Based on DEA and SVM, the paper proposes a method for...
Wavelet packet analysis method is appropriate to process nature texture signal and a hidden Markov model has good learning interpretability and needs only small training samples. A wavelet packet-HMM-based method on road surface state recognition was proposed. The wavelet packet analysis was adapted to extract characteristic entropies from the image signals. Thus, four kinds of data on road surface...
In this paper, we formulate image annotation as a semi-supervised learning problem under multi-instance learning framework. A novel graph based semi-supervised learning approach to image annotation using multiple instances is presented, which extends the conventional semi-supervised learning to multi-instance setting by introducing the adaptive geometric relationship between two bags of instances...
Sentiment classification is an applied technology with great significance. It can help people find right reviews in a more efficient way. In this paper, we present a novel efficient method for BBS sentiment classification. Through extracting sentiment-bearing words from WordNet using the maximum entropy, a ranking criterion based on a function of the probability of having Polarity or not is introduced...
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