Maximum and Minimum Likelihood Hebbian Learning for Exploratory Projection Pursuit

In this paper, we review an extension of the learning rules in a Principal Component Analysis network which has been derived to be optimal for a specific probability density function. We note that this probability density function is one of a family of pdfs and investigate the learning rules formed in order to be optimal for several members of this family. We show that, whereas we have previously (Lai et al., 2000; Fyfe and MacDonald, 2002) viewed the single member of the family as an extension of PCA, it is more appropriate to view the whole family of learning rules as methods of performing Exploratory Projection Pursuit. We illustrate this on both artificial and real data sets.     

Connectionist projection pursuit regression

We present a novel regression method that combines projection pursuit regression with feed forward neural networks. The algorithm is presented and compared to standard neural network learning. Connectionist projection pursuit regression (CPPR) is applied to modelling the U.S. average dollar-Deutsch mark exchange rate movement using several economic indicators. The performance of CPPR is compared with the performances of other approaches to this problem.     

Combining topographic map formation with projection pursuit learning for nonparametric regression analysis

The original Self-Organizing Map (SOM) algorithm is known to perform poorly on regression problems due to the occurrence of nonfunctional mappings. Recently, we have introduced an unsupervised learning rule, called the Maximum Entropy learning Rule (MER), which performs topographic map formation without using a neighborhood function. In the present paper, MER is extended with a neighborhood function and applied to nonparametric projection pursuit regression. The extended rule, called eMER, alleviates the occurrence of nonfunctional mappings. The performance of our regression procedure is quantified and compared to other neural network-based parametric and nonparametric regression procedures.     

基于小波域投影寻踪网络的多源退化图像的恢复

针对多源退化图像的恢复问题,提出小波域投影寻踪网络,并用于图像恢复。这种新方法结合了投影寻踪理论和小波收缩技术的优点,通过分别处理小波系数和尺度系数,较好地解决了对于多源退化因子的先验知识知之甚少情况下图像恢复这一难题。采用投影寻踪网络对模糊源进行模拟,估计退化因子;同时,用小波收缩技术中的软阈值方法来对噪声源进行抑噪处理。实验的结果与传统的逆滤波方法及投影寻踪网络方法相应的结果比较,说明这种新方法是一种有效的多源退化图像的恢复方法。     

GEAMAS: A Generic Architecture for Agent-Oriented Simulations of Complex Processes

This paper's object is to present the results of the GEAMAS project which aims at modeling and simulating natural complex systems. GEAMAS is a generic architecture of agents used to study the behavior emergence in such systems. It is a multiagent program meant to develop simulation applications. Modeling complex systems requires to reduce, to organize the system complexity and to describe suitable components. Complexity of the system can then be tackled with an agent-oriented approach, where interactions lead to a global behavior. This approach helps in understanding how non-determinist behavior can emerge from interactions between agents, which is near of self-organized criticality used to explain natural phenomena. In the Applied Artificial Intelligence context, this paper presents an agent software architecture using a model of agent. This architecture is composed of three abstract levels over which the complexity is distributed and reduced. The architecture is implemented in ReActalk, an open agent-oriented development tool, which was developed on top of Smalltalk-80. To illustrate our purpose and to validate the architecture, a simulation program to help in predicting volcanic eruptions was investigated. This program was run over a period of one year and has given many satisfying results unattainable up to there with more classical approaches.     

Learning and Exploiting Relative Weaknesses of Opponent Agents

Agents in a competitive interaction can greatly benefit from adapting to a particular adversary, rather than using the same general strategy against all opponents. One method of such adaptation isOpponent Modeling, in which a model of an opponent is acquired and utilized as part of the agents decision procedure in future interactions with this opponent. However, acquiring an accurate model of a complex opponent strategy may be computationally infeasible. In addition, if the learned model is not accurate, then using it to predict the opponents actions may potentially harm the agents strategy rather than improving it. We thus define the concept ofopponent weakness, and present a method for learning a model of this simpler concept. We analyze examples of past behavior of an opponent in a particular domain, judging its actions using a trusted judge. We then infer aweakness model based on the opponents actions relative to the domain state, and incorporate this model into our agents decision procedure. We also make use of a similar self-weakness model, allowing the agent to prefer states in which the opponent is weak and our agent strong; where we have arelative advantage over the opponent. Experimental results spanning two different test domains demonstrate the agents improved performance when making use of the weakness models.     

基于标签分类的协同过滤推荐算法

传统的协同过滤根据用户的行为去预测可能喜欢的产品,是当前应用最广泛的推荐算法之一。但随着用户规模的急剧扩大,有价值的信息占比较少,存在稀疏性等问题,导致推荐质量不高。针对这一问题,提出了一种基于标签分类的协同过滤推荐算法。将不完整的数据样本根据标签进行分类,使分解的矩阵依赖于类,随后使用迭代投影寻踪的方法计算类依赖矩阵的线性组合及其对应的权重。开放数据集实验表明,该方法在保持一定分类准确率的前提下,平均降低了35.23%的插补误差,优于传统协同过滤推荐算法。     

On an on-line dynamic direct coordination method in process industry

A two-level production control system based on an on-line dynamic direct coordination method is discussed. Within the structure, each local unit makes decisions keeping to the coupling outputs as recommended by the coordinator. An application to a system in process industry is given.     

车辆跟驰投影寻踪回归模型

车辆跟驰模型是微观交通仿真的一个基本模型,基于非参数回归算法的跟车模型较好地解决了以往模型存在的典型问题,但随着样本维数增加,容易出现“维数祸根”现象。提出一种基于投影寻踪回归（PPR）技术的车辆跟驰模型,解决了“维数祸根”和高维数据间的非正态、非线性问题。PPR建模不需要对数据结构作任何假定,而只通过直接审视和分析数据进行建模,因此,该方法能充分地发掘数据中存在的信息,建立的模型符合客观实际,精度较高。经过实测数据验证,该算法用于车辆跟驰模型的研究是可行的。     

The Latent Variable Data Model for Exploratory Data Analysis and Visualisation: A Generalisation of the Nonlinear Infomax Algorithm

This paper presents a generalisation of the nonlinear 'Infomax' algorithm based on the linear latent variable model of factor analysis. The algorithm is based on an information theoretic index for projection pursuit which defines linear projections of observed data onto subspaces of lower dimension. This is applied to the visualisation and interpretation of complex high dimensional data and is empirically compared with the recently developed Generative Topographic Mapping.     

Matlab遗传算法工具箱在非线性优化中的应用

投影寻踪是一种降维处理技术,通过它可以将多维分析问题通过投影方向转化为一维问题分析。应用该法的关键在于寻求最佳投影方向,这可以转化为一个复杂的非线性优化问题来进行解决。选取某地区大气环境质量评价的投影寻踪评价模型,编制相关的目标函数和约束函数,应用基于MATLAB的遗传算法和直接搜索工具箱进行优化求解。结果表明：该工具箱在求解此类非线性优化问题上的有效性和方便性,从而为各领域应用投影寻踪模型提供了强有力的优化工具。     

DEXTER: A System that Experiments with Choices of Training Data Using Expert Knowledge in the Domain of DNA Hydration

In this paper, we describe a system, DEXTER, that uses knowledge to suggest inductive learning experiments in the domain of DNA hydration pattern prediction. These experiments vary the training data presented to a classifier learner. Such experiments are necessary in this domain, since, as in many other scientific domains, data are noisy, the relevance of particular attributes is not well established, and the number of training cases is limited. In each experiment, DEXTER chooses a set of training cases, attributes and classes to learn. To generate an experiment, it examines the results of previous experiments, and uses domain knowledge and domain independent heuristics to select and modify a previous experiment. For the domain expert interested in using the induced rules to understand data, DEXTER's explicit use of knowledge provides several advantages that other data selection techniques do not. In particular, the variation of classifiers induced in different experiments yields insights into the roles and interactions of particular attributes in determining hydration. In addition, many of the classifiers induced from DEXTER's choices of data are of accuracy greater than or equal to those induced using the entire set of available data or data chosen by several other techniques. This work is of theoretical and pragmatic importance to molecular biophysicists. The learned hydration predictors provide insights about factors influencing DNA hydration. Also, the hydration predictors could lead to a tool for automatically predicting water positions around DNA molecules for which crystallographic data are not available.     

Decision Planning Knowledge Representation Framework: A Case-Study

This paper discusses experiences and perspectives of utilisation of declarative knowledge structures as a convenient knowledge base medium in configuration expert systems. Although many successful systems have been developed, these are often difficult to maintain and to generalize in rapidly changing domains. In this paper we address the problem of building intelligent knowledge based systems with emphasis on their maintainability. Firstly, several industrial applications of proof planning, a theorem proving technique, will be described and their advantages and flaws will be discussed. This discussion is followed by the theoretical foundation of decision planning knowledge representation framework that, based on proof planning, facilitates separate administration of inference problem solving knowledge and the domain theory axioms. Machine learning methods for maintaining the inference knowledge to be up-to-date with permanently changing domain theory are commented and evaluated.     

An industrial visual inspection system that uses inductive learning

This paper presents an industrial visual inspection system that uses inductive learning. The system employs RULES-3 inductive learning algorithm to extract the necessary set of rules and template matching technique to process an image. Twenty 3×3 masks are used to represent an image. Each example consists of 20 frequencies of each mask. The system was tested on five different types of tea or water cups in order to classify the good and bad items. The system was trained using five good cups and then tested for 113 unseen examples. The results obtained showed the high performance of the system: the efficiency of the system for correctly classifying unseen examples was 100%. The system can also decide what type of the cup is being processed.     

Real-time object detection and localization with SIFT-based clustering

This paper presents an innovative approach for detecting and localizing duplicate objects in pick-and-place applications under extreme conditions of occlusion, where standard appearance-based approaches are likely to be ineffective. The approach exploits SIFT keypoint extraction and mean shift clustering to partition the correspondences between the object model and the image onto different potential object instances with real-time performance. Then, the hypotheses of the object shape are validated by a projection with a fast Euclidean transform of some delimiting points onto the current image. Moreover, in order to improve the detection in the case of reflective or transparent objects, multiple object models (of both the same and different faces of the object) are used and fused together. Many measures of efficacy and efficiency are provided on random disposals of heavily-occluded objects, with a specific focus on real-time processing. Experimental results on different and challenging kinds of objects are reported.     

遥感影像投影寻踪回归分类模型

将投影寻踪回归分析技术引入遥感影像分类中,详尽叙述遥感影像投影寻踪回归分类模型的建立和实现过程。将广州地区的TM影像用于分类实验,并用混合蛙跳算法来优化投影寻踪回归分类模型中的参数矩阵,取得了较为理想的分类效果。此外,还进一步分析了投影中心的设定、调整以及优化算法和岭函数个数对投影寻踪回归模型分类精度的影响。实验结果表明,该模型易于优化实现,稳定性强,模型中岭函数的个数对投影寻踪回归模型的分类精度没有显著影响。     

Explanation-Based Learning: A survey

The paper provides an introductory survey of Explanation-Based Learning (EBL). It attempts to define EBL's position in AI by exploring its relationship to other AI techniques, including other sub-fields of machine learning. Further issues discussed include the form of learning exhibited by EBL and potential applications of the method.