Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Deep learning has been successfully applied in several fields such as machine translation, manufacturing, and pattern recognition. However, successful application of deep learning depends upon appropriately setting its parameters to achieve high-quality results. The number of hidden layers and the number of neurons in each layer of a deep machine learning network are two key parameters, which have main influence on the performance of the algorithm. Manual parameter setting and grid search approaches somewhat ease the users' tasks in setting these important parameters. Nonetheless, these two techniques can be very time-consuming. In this paper, we show that the Particle swarm optimization (PSO) technique holds great potential to optimize parameter settings and thus saves valuable computational resources during the tuning process of deep learning models. Specifically, we use a dataset collected from a Wi-Fi campus network to train deep learning models to predict the number of occupants and their locations. Our preliminary experiments indicate that PSO provides an efficient approach for tuning the optimal number of hidden layers and the number of neurons in each layer of the deep learning algorithm when compared to the grid search method. Our experiments illustrate that the exploration process of the landscape of configurations to find the optimal parameters is decreased by 77 % – 85%. In fact, the PSO yields even better accuracy results.

Parameters optimization of deep learning models using Particle swarm optimization

The Artificial Bee Colony (ABC) is a recently introduced swarm intelligence algorithm for optimization, that has previously been applied successfully to the training of neural networks. This paper explores more carefully the performance of the ABC algorithm for optimizing the connection weights of feed-forward neural networks for classification tasks, and presents a more rigorous comparison with the traditional Back-Propagation (BP) training algorithm. The empirical results show that using the standard “stopping early” approach with optimized learning parameters leads to improved BP performance over the previous comparative study, and that a simple variation of the ABC approach provides improved ABC performance too. With both improvements applied, we conclude that the ABC approach does perform very well on small problems, but the generalization performances achieved are only significantly better than standard BP on one out of six datasets, and the training times increase rapidly as the size of the problem grows.

/pdf/artificial-bee-colony-training-of-neural-networks-zpoay9l5nk.pdf

Artificial Bee Colony Training of Neural Networks

The Artificial Bee Colony (ABC) is a swarm intelligence algorithm for optimization that has previously been applied to the training of neural networks This paper examines more carefully the performance of the ABC algo- rithm for optimizing the connection weights of feed-forward neural networks for classification tasks, and presents a more rigorous comparison with the traditional Back-Propagation (BP) training algorithm The empirical results for bench- mark problems demonstrate that using the standard "stop- ping early" approach with optimized learning parameters leads to improved BP performance over the previous com- parative study, and that a simple variation of the ABC approach provides improved ABC performance too With bothimprovementsapplied,theABCapproachdoesperform very well on small problems, but the generalization perfor- mances achieved are only significantly better than standard BP on one out of six datasets, and the training times increase rapidly as the size of the problem grows If different, evo- lutionary optimized, BP learning rates are allowed for the two layers of the neural network, BP is significantly better than the ABC on two of the six datasets, and not significantly different on the other four

/pdf/artificial-bee-colony-training-of-neural-networks-comparison-p9rc4grghb.pdf

Artificial Bee Colony training of neural networks: comparison with back-propagation

Collaborative filtering has become one of the most widely used methods for a variety of commercial recommendations. The key to collaborative filtering is use similarity calculation formula to find similar neighbors or projects. However, most similarity calculation methods only use the user common score and provide bad recommendations. This paper proposes a new similarity measure method, which effectively utilizes the user context information. The new method uses a singularity factor to adjust nonlinear equation and takes into account the user scoring habits. It can improve the accuracy of the prediction. The new method has been tested on the dataset and compared with other algorithms. The results show that the proposed method can improve the recommendation quality.

/pdf/a-new-similarity-computing-model-of-collaborative-filtering-1swq80nwoe.pdf

A New Similarity Computing Model of Collaborative Filtering

After studying the disadvantage of BP neural network which has low convergent speed and trap into local minima easily, an idea of designing a new hybrid neural network model. By using Artificial Bee Colony Algorithm (ABC) to expand the updated space of weight and using the fitness functions to decide the better weight. On the basis, make the acquired better value as the weight of BP neural network. Both are coordination makes the optimized BP network not to trap into the local minima, and good generalization characteristic. Simulation analysis demonstrated that this network model can attain higher categories of precision.

A hybrid model of neural network and classification in wine

The security companies in stocks transactions have accumulated a large number of data.It is very important to making decision for investment to effectively use the data and find the interactional relation between the data.This paper constructs the model in stocks transactions and gives the data pre-process algorithms and implements the mining algorithm of such rules.The model and algorithm are proved to be efficient by experiment and helpful to investment.

Application and Research of Association Rules in Stocks Analysis

Considering the drawbacks of the recent remote attestation schemes,such as easy exposure of the platform configurations information,the complexity of the attestation certificates management,a method property-based Chameleon remote attestation is proposed based on the idea of property-based attestation.PBCRA uses Chameleon hash function to improve the attestation process,and uses the anti-collision character of the Chameleon hash function to protect the platform configuration information,the results of Chameleon hash function are untilzed as the prove of platform attributes in the remote attestation process to address the issues caused by attestation certificates.Analysis shows that the method enhances the flexibility and security of the remote attestation.

Property-based chameleon remote attestation

After studying the disadvantage of BP neural network which has low convergent speed and trap into local minima easily,an idea of designing a new hybrid neural network model which adopts the method of numerical optimization is presented.By using Genetic-Stimulated Annealing algorithm(GSA),expands the updated space of weight.On the basis,it makes the acquired better value as the weight of BP neural network,and the optimized BP network is not easy to trap into the local minima and has good generalization characteristic.Making the comparation GSA network with standard BP network,simulation analysis demonstrates that this network model can attain higher categories of precision.

BP Neural Network Optimization Algorithm Based on Genetic-stimulated Annealing

As the Electronic data of goods and services expanding every day, collaborative filtering (CF) has become a popular and attractive technique in recommender systems In this paper, a hybrid approach is proposed to solve problems which are challenges of the collaborative filtering, such as data sparsity, accurate of similarities The experimental results show that our hybrid method can efficiently improve the extreme sparsity of user rating data, and improve the accurate of similarities in some extent

Wang Shi-qing

Papers

A hybrid model of neural network and classification in wine

Application and Research of Association Rules in Stocks Analysis

Property-based chameleon remote attestation

BP Neural Network Optimization Algorithm Based on Genetic-stimulated Annealing

Hybrid Collaborative Filtering Recommendation Algorithm Based on Model Filling