Aerial Vehicles follow a guided approach based on Latitude, Longitude and
Altitude. This information can be used for calculating the status of
maneuvering for the aerial vehicles along the line of trajectory. This is a
binary classification problem and Machine Learning can be leveraged for solving
such problem. In this paper we present a methodology for deriving maneuvering
status and its prediction using Linear, Distance Metric, Discriminant Analysis
and Boosting Ensemble supervised learning methods. We provide various metrics
along the line in the results section that give condensed comparison of the
appropriate algorithm for prediction of the maneuvering status.

本文提供了一种使用线性，距离度量，鉴别分析和提升集成有监督学习方法推导机动状况及其预测的方法，并通过结果部分提供了各种指标，以便针对机动状况预测选择适当的算法。

使用监督学习方法预测航空器机动状态

Prediction of Maneuvering Status for Aerial Vehicles using Supervised  Learning Methods

A lot of prognostication methodologies have been formulated for early
detection of Polycystic Ovary Syndrome also known as PCOS using Machine
Learning. PCOS is a binary classification problem. Dimensionality Reduction
methods impact the performance of Machine Learning to a greater extent and
using a Supervised Dimensionality Reduction method can give us a new edge to
tackle this problem. In this paper we present Discriminant Analysis in
different dimensions with Linear and Quadratic form for binary classification
along with metrics. We were able to achieve good accuracy and less variation
with Discriminant Analysis as compared to many commonly used classification
algorithms with training accuracy reaching 97.37% and testing accuracy of
95.92% using Quadratic Discriminant Analysis. Paper also gives the analysis of
data with visualizations for deeper understanding of problem.

本研究旨在利用监督降维方法中的判别分析来检测多囊卵巢综合症（PCOS）， 并使用线性和二次判别分析形式进行二元分类，同时给出了可视化数据分析，分类准确率可达 97.37%。

对多囊卵巢综合征预测中尺度对比判别分析

Discriminant Analysis in Contrasting Dimensions for Polycystic Ovary  Syndrome Prognostication

In practice, the data distribution at test time often differs, to a smaller
or larger extent, from that of the original training data. Consequentially, the
so-called source classifier, trained on the available labelled data,
deteriorates on the test, or target, data. Domain adaptive classifiers aim to
combat this problem, but typically assume some particular form of domain shift.
Most are not robust to violations of domain shift assumptions and may even
perform worse than their non-adaptive counterparts. We construct robust
parameter estimators for discriminant analysis that guarantee performance
improvements of the adaptive classifier over the non-adaptive source
classifier.

本文尝试通过构建具有鲁棒性的参数估计器，以实现域自适应分类器性能超越非自适应源分类器的目的，关键词包括领域自适应、分类器、鲁棒参数估计器、判别分析和性能提升。

目标鲁棒判别分析

Target Robust Discriminant Analysis

Face images captured in heterogeneous environments, e.g., sketches generated
by the artists or composite-generation software, photos taken by common cameras
and infrared images captured by corresponding infrared imaging devices, usually
subject to large texture (i.e., style) differences. This results in heavily
degraded performance of conventional face recognition methods in comparison
with the performance on images captured in homogeneous environments. In this
paper, we propose a novel sparse graphical representation based discriminant
analysis (SGR-DA) approach to address aforementioned face recognition in
heterogeneous scenarios. An adaptive sparse graphical representation scheme is
designed to represent heterogeneous face images, where a Markov networks model
is constructed to generate adaptive sparse vectors. To handle the complex
facial structure and further improve the discriminability, a spatial
partition-based discriminant analysis framework is presented to refine the
adaptive sparse vectors for face matching. We conducted experiments on six
commonly used heterogeneous face datasets and experimental results illustrate
that our proposed SGR-DA approach achieves superior performance in comparison
with state-of-the-art methods.

本文提出一种基于稀疏图形表示的判别分析方法，针对异构人脸识别场景下出现的纹理差异等问题，设计了自适应的稀疏图形表示方案，通过 Markov 网络生成自适应稀疏向量；并引入基于空间分块的判别分析方法以优化自适应稀疏向量，实验证明该方法在 6 个常用异构人脸数据集上达到了优异表现。