摘要翻译：
机器学习领域中的许多问题都涉及到非线性问题，往往要求用户获得全局最优解而不是局部最优解。优化问题是机器学习算法中固有的问题，因此机器学习中的许多方法都是从优化文献中继承而来的。通常称为初始化问题，所需的理想参数集将在很大程度上取决于给定的初始化值。最近发展起来的TRUST-TECH（稳定性保持平衡特征下的变换）方法系统地探索参数的子空间，以获得一组完整的局部最优解。在本论文的工作中，我们提出了基于信任技术的方法来解决几个优化和机器学习问题。在解空间中交替重复两个阶段，即局部阶段和邻域搜索阶段，以提高解的质量。我们的方法在合成数据集和真实数据集上进行了测试，使用这种新框架的优势得到了明显的体现。该框架不仅降低了初始化的敏感性，而且允许实践者灵活地使用各种全局和局部方法，这些方法可以很好地解决特定的感兴趣的问题。本文还研究了其他层次随机算法，如进化算法和平滑算法，提出了将这些方法与信任技术相结合的框架，并在几个测试系统上进行了评估。
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英文标题：
《TRUST-TECH based Methods for Optimization and Learning》
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作者：
Chandan K. Reddy
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最新提交年份：
2007
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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一级分类：Computer Science        计算机科学
二级分类：Computational Engineering, Finance, and Science        计算工程、金融和科学
分类描述：Covers applications of computer science to the mathematical modeling of complex systems in the fields of science, engineering, and finance. Papers here are interdisciplinary and applications-oriented, focusing on techniques and tools that enable challenging computational simulations to be performed, for which the use of supercomputers or distributed computing platforms is often required. Includes material in ACM Subject Classes J.2, J.3, and J.4 (economics).
涵盖了计算机科学在科学、工程和金融领域复杂系统的数学建模中的应用。这里的论文是跨学科和面向应用的，集中在技术和工具，使挑战性的计算模拟能够执行，其中往往需要使用超级计算机或分布式计算平台。包括ACM学科课程J.2、J.3和J.4（经济学）中的材料。
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一级分类：Computer Science        计算机科学
二级分类：Mathematical Software        数学软件
分类描述：Roughly includes material in ACM Subject Class G.4.
大致包括ACM学科类G.4的材料。
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一级分类：Computer Science        计算机科学
二级分类：Numerical Analysis        数值分析
分类描述：cs.NA is an alias for math.NA. Roughly includes material in ACM Subject Class G.1.
cs.na是Math.na的别名。大致包括ACM学科类G.1的材料。
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一级分类：Computer Science        计算机科学
二级分类：Neural and Evolutionary Computing        神经与进化计算
分类描述：Covers neural networks, connectionism, genetic algorithms, artificial life, adaptive behavior. Roughly includes some material in ACM Subject Class C.1.3, I.2.6, I.5.
涵盖神经网络，连接主义，遗传算法，人工生命，自适应行为。大致包括ACM学科类C.1.3、I.2.6、I.5中的一些材料。
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英文摘要：
  Many problems that arise in machine learning domain deal with nonlinearity and quite often demand users to obtain global optimal solutions rather than local optimal ones. Optimization problems are inherent in machine learning algorithms and hence many methods in machine learning were inherited from the optimization literature. Popularly known as the initialization problem, the ideal set of parameters required will significantly depend on the given initialization values. The recently developed TRUST-TECH (TRansformation Under STability-reTaining Equilibria CHaracterization) methodology systematically explores the subspace of the parameters to obtain a complete set of local optimal solutions. In this thesis work, we propose TRUST-TECH based methods for solving several optimization and machine learning problems. Two stages namely, the local stage and the neighborhood-search stage, are repeated alternatively in the solution space to achieve improvements in the quality of the solutions. Our methods were tested on both synthetic and real datasets and the advantages of using this novel framework are clearly manifested. This framework not only reduces the sensitivity to initialization, but also allows the flexibility for the practitioners to use various global and local methods that work well for a particular problem of interest. Other hierarchical stochastic algorithms like evolutionary algorithms and smoothing algorithms are also studied and frameworks for combining these methods with TRUST-TECH have been proposed and evaluated on several test systems.
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PDF链接：
https://arxiv.org/pdf/0712.4126