Biologically Inspired Intelligent Classifiers

该项目将促进智能设计的知识 剂. 它有两个目标。 第一个目标是 发展数学见解和自动化程序 特征发现和进化学习 长期学习的启发形式-在建设 自动模式分类器。 第二个目标是 将自动特征发现和演化结合到 大规模图像数据库的自动分析仪， 时变视觉信号 该项目将致力于实现 通过建立在主要研究者和 他的学生的技术，自适应超平面放置，窗口 训练（随机近似的扩展形式），放松 分支定界搜索和遗传特征选择。 的 项目将实施和测试由此产生的机制， 手写体三阶段神经分类器的长期学习 数字特征，树分类器的医学图像，在一个大的 数据库和数字化中移动物体的自适应分割器 视频. 该项目将有助于长期的技术 智能机器学习和多传感器机器人视觉 通过自动发现高分辨力的组合， 各种传感器的特征

This project will advance knowledge about design of intelligent agents. It has two objectives. The first objective is the development of mathematical insights and procedures for automating feature discovery and learning by evolution -- two biologically inspired forms of long-term learning -- in the construction of automatic pattern classifiers. The second objective is the incorporation of automated feature discovery and evolution in automatic analyzers of large-scale image data banks and time-varying visual signals. This project will work to achieve these objectives by building on the Principal Investigator's and his students' techniques of adaptive hyperplane placement, window training (an extended form of stochastic approximation), relaxed branch-and-bound search, and genetic feature selection. The project will implement and test the resulting mechanisms of long-term learning in three-stage neural classifiers of handwritten numerical characters, tree classifiers of medical images in a large data bank, and adaptive segmenter of moving objects in digitized video. This project will contribute to the technology of long term learning in intelligent machines and to multi-sensor robotic vision by automating the discovery of highly discriminating combinations of features from diverse sensors.