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Model-free reinforcement learning algorithms based on POMDP has been devised and adopted for many years. The complexity of the environment where the agent works determines the necessity of dealing with the whole observation space. Therefore, instance-based learning methods have been put forward. NSM, USM and U-Tree algorithms can present the whole observation space as instance chains, which are very...
As an online learning algorithm of approximate dynamic programming (ADP), direct heuristic dynamic programming (DHDP) has demonstrated its applicability to large state and control problems. However, there still lacks of a systemic approach to initialize the network weights for DHDP. In this paper, an improved PID-neural network (IPIDNN) configuration is proposed and applied to the critic and action...
The Research of detection malware using machine learning method attracts much attention recent years. However, most of research focused on code analysis which is signature-based or analysis of system call sequence in Linux environment. Obviously, all methods have their strengths and weaknesses. In this paper, we concentrate on detection Trojan horse by operation system information in Windows environment...
This paper presents a storehouse boundary warning system based on multi-sensor information fusion technology. This warning system has fire detection multiple sensor data fusion algorithm based on a fuzzy neural network to compute fire emergency probability. According to the network training and self-learning weight adaptation, the error is least between the output and instruction signal. This allows...
Issues associated with data transmission in sensing networks via either cabling or single wireless medium are investigated, e.g. installation inconvenience, bad stability, etc. A sophisticated wireless network, wireless sensing network (WSN) possesses a large amount of nodes whereby a sensing base is formed. In this research, neuron concept and its mathematical model are used to depict network nodes...
These years, P2P applications have multiplied, evolved and take a big part of Internet traffic workload. Identifying the P2P traffic and understanding their behavior is an important field. Some port, payload and transport layer feature based methods were proposed. P2P traffic identification methods by examining user payload or well-defined port numbers no longer adapt to current P2P applications....
In real worlds applications, some former research papers have shown that manifold learning tries to discover the non-linear low-dimensional data manifold from a high-dimensional space. Many natural images and the face images are believed to be sampled from a manifold. In this paper, we try to investigate whether discovering such manifold can aid the semi-supervised learning algorithms. We propose...
Existing algorithms for learning Bayesian network require a lot of computation on high dimensional itemsets which affects accuracy especially on limited datasets and takes up a large amount of time. To address the above problem, we propose a novel Bayesian network learning algorithm MRMRG, Max Relevance-Min Redundancy Greedy. MRMRG algorithm is a variant of K2 which is a well- known BN learning algorithm...
This paper proposes an improved boost learning algorithm, the SceBoost algorithm, and its application in developing fast and robust features for citrus canker detection by machine vision. The algorithm use symmetric cross entropy to eliminate redundancy among selected features using AdaBoost algorithm. Selected features are subjected to recognize citrus canker symptoms on given pictures of citrus...
Heuristic hill-climbing search algorithm can do effectively pruning. In practice, it can be used to search a large hypothesis space to get an optimal or an approximate optimal solution. Beam search algorithm retains its advantage in efficiency while reducing the risk of converging to locally optimal hypotheses. Beam search algorithm is widely used in AI field. To k-size beam search, due to only k...
Gaussian mixture model (GMM) is a popular tool for density estimation. The parameters of the GMM are estimated based on maximum likelihood principle (MLP) in almost all recognition system. However, the number of mixtures used in the model is important for determining the model's effectiveness; the general problem of mixture modeling is difficult when the number of components is unknown. This paper...
This paper extends the idea of weighted distance functions to kernels and support vector machines. Here, we focus on applications that rely on sliding a window over a sequence of string data. For this type of problems it is argued that a symbolic, context-based representation of the data should be preferred over a continuous, real format as this is a much more intuitive setting for working with (weighted)...
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