In the era of modern technology, object detection using the Gray Level
Co-occurrence Matrix (GLCM) extraction method plays a crucial role in object
recognition processes. It finds applications in real-time scenarios such as
security surveillance and autonomous vehicle navigation, among others.
Computational efficiency becomes a critical factor in achieving real-time
object detection. Hence, there is a need for a detection model with low
complexity and satisfactory accuracy. This research aims to enhance
computational efficiency by selecting appropriate features within the GLCM
framework. Two classification models, namely K-Nearest Neighbours (K-NN) and
Support Vector Machine (SVM), were employed, with the results indicating that
K-Nearest Neighbours (K-NN) outperforms SVM in terms of computational
complexity. Specifically, K-NN, when utilizing a combination of Correlation,
Energy, and Homogeneity features, achieves a 100% accuracy rate with low
complexity. Moreover, when using a combination of Energy and Homogeneity
features, K-NN attains an almost perfect accuracy level of 99.9889%, while
maintaining low complexity. On the other hand, despite SVM achieving 100%
accuracy in certain feature combinations, its high or very high complexity can
pose challenges, particularly in real-time applications. Therefore, based on
the trade-off between accuracy and complexity, the K-NN model with a
combination of Correlation, Energy, and Homogeneity features emerges as a more
suitable choice for real-time applications that demand high accuracy and low
complexity. This research provides valuable insights for optimizing object
detection in various applications requiring both high accuracy and rapid
responsiveness.

在现代技术时代，利用灰度共生矩阵（GLCM）提取方法的物体检测在物体识别过程中起着至关重要的作用，该研究通过选择 GLCM 框架内适当的特征来提高计算效率，发现 K-NN 模型使用相关性、能量和均匀性特征组合可以达到 100% 的准确率和低复杂度，因此在需要高准确率和低复杂度的实时应用中，K-NN 模型是更合适的选择。

基于 GLCM 特征组合的机器学习目标检测模型优化

GLCM-Based Feature Combination for Extraction Model Optimization in  Object Detection Using Machine Learning

We study gradient flow on the exponential loss for a classification problem
with a one-layer softmax attention model, where the key and query weight
matrices are trained separately. Under a separability assumption on the data,
we show that when gradient flow achieves the minimal loss value, it further
implicitly minimizes the nuclear norm of the product of the key and query
weight matrices. Such implicit regularization can be described by a Support
Vector Machine (SVM) problem with respect to the attention weights. This
finding contrasts with prior results showing that the gradient descent induces
an implicit regularization on the Frobenius norm on the product weight matrix
when the key and query matrices are combined into a single weight matrix for
training. For diagonal key and query matrices, our analysis builds upon the
reparameterization technique and exploits approximate KKT conditions of the SVM
associated with the classification data. Moreover, the results are extended to
general weights configurations given proper alignment of the weight matrices'
singular spaces with the data features at initialization.

研究一层 softmax 注意模型上指数损失的梯度流问题，通过分离地训练键值矩阵，我们在数据可分性假设下证明梯度流在达到最小损失值时，进一步隐式地最小化键值矩阵乘积的核范数，这种隐式正则化可以用关注权重的支持向量机（SVM）问题来描述。与先前结果相反，当将键值矩阵合并为单个权重矩阵进行训练时，梯度下降对于乘积权重矩阵的 Frobenius 范数引入了隐式正则化。对于对角键值和查询矩阵，我们的分析建立在重新参数化技术和利用与分类数据相关的 SVM 近似 KKT 条件的基础上。此外，该结果对于给定适当对齐的权重矩阵奇异空间与数据特征的初始化情况也同样适用。

一层 Softmax 注意力梯度流的隐性正则化

Implicit Regularization of Gradient Flow on One-Layer Softmax Attention

Gaussian Radial Basis Function (RBF) Kernels are the most-often-employed
kernels in artificial intelligence and machine learning routines for providing
optimally-best results in contrast to their respective counter-parts. However,
a little is known about the application of the Generalized Gaussian Radial
Basis Function on various machine learning algorithms namely, kernel
regression, support vector machine (SVM) and pattern-recognition via neural
networks. The results that are yielded by Generalized Gaussian RBF in the
kernel sense outperforms in stark contrast to Gaussian RBF Kernel, Sigmoid
Function and ReLU Function. This manuscript demonstrates the application of the
Generalized Gaussian RBF in the kernel sense on the aforementioned machine
learning routines along with the comparisons against the aforementioned
functions as well.

应用广义高斯径向基函数（RBF）在机器学习算法中的性能优于其他函数，通过对比高斯 RBF 核函数、Sigmoid 函数和 ReLU 函数，本文展示了广义高斯 RBF 在核函数中的应用及其在核回归、支持向量机（SVM）和模式识别中的成果。