支持向量机训练算法：Sequential Minimal Optimization（SMO）

机器学习
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"Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines" 本文主要介绍了一种用于训练支持向量机（Support Vector Machines, SVM）的新算法——Sequential Minimal Optimization（SMO）。SMO是由John C. Platt在1998年提出的，旨在解决支持向量机训练中的大规模二次规划（Quadratic Programming, QP）优化问题，从而提高训练速度和模型的准确性。 支持向量机是一种广泛应用于分类和回归任务的监督学习模型，其核心在于寻找一个超平面，使得不同类别的样本点距离这个超平面的距离最大化。在训练过程中，需要求解一个复杂的QP问题，这通常涉及到大量的计算和内存需求。SMO算法通过将大问题分解成一系列最小的QP子问题来解决这一难题。 SMO算法的独特之处在于它将大QP问题分解为两个变量的优化问题，然后通过解析解求解这些小问题，避免了使用数值优化方法作为内部循环，从而显著减少了计算时间。这种策略使得SMO对内存的需求线性依赖于训练集的大小，因此可以处理大规模的训练数据。 在实际应用中，SMO的运行时间主要由支持向量的评估决定。对于线性SVM和稀疏数据集，SMO表现得尤为高效，因为这类问题中的支持向量评估更快。相比之下，标准的分块SVM算法的复杂度在训练集大小上介于线性和立方之间，对于大规模数据集来说，SMO的效率优势更为明显。 此外，SMO的性能在不同的测试问题中表现出介于线性与二次之间的扩展性，而传统的SVM算法则在最坏情况下可能达到三次方的复杂度。这意味着，对于大规模数据，SMO在保持高效的同时，也能保证较高的精度，对于机器学习领域，尤其是需要处理大量数据的问题，SMO算法的提出是一个重要的进步。

SMO英文原文

2017-07-23 上传
SMO原文SequentialMinimalOptimization-AFastAlgorithmforTrainingSupportVectorMachines.pdf

Sequential Minimal Optimization for SVM

2012-10-30 上传
采用SMO算法对两类文件进行分类，最终的分类精度高达97%

论文Sequential Minimal Optimization for SVM（smo）--有smo程序

2010-04-05 上传
论文Sequential Minimal Optimization for SVM 里面有C++写的smo程序及比较详细的讲解。（英文版）

Exercise#1: Decision Tree and support vector machine. Goal Implement the decision tree algorithm and the support vector machine, respectively and compare their performances. Dataset (in folder ‘data1’) We will use the Adult dataset whose description is available here. The training set (adult.data) and evaluation set (adult.test) can be downloaded here. The prediction task is to determine whether a person makes over 50K a year. Preprocessing Remove all the records containing '?' (i.e., missing values). Also, remove the attribute "native-country". you can not use pandas， DecisionTreeClassifier and SVC

2023-05-12 上传
Finally, let's implement the support vector machine algorithm using the sequential minimal optimization (SMO) algorithm. ```python # Calculate dot product of two vectors def dot_product(x, y): ...

SVM的提出的参考文献

2023-04-03 上传
Fast training of support vector machines using sequential minimal optimization. Advances in kernel methods-support vector learning, 185-208. 8. Joachims, T. (1998). Text categorization with support ...

Task: - Download the UCM dataset from the official source or any reputable repository. - Preprocess the dataset by splitting it into training, validation, and test sets. Please use an 80-10-10 split, respectively. - Choose a suitable deep-learning algorithm for scene classification. You can use popular classifiers such as Convolutional Neural Networks (CNNs). - Implement the chosen algorithm in Python, using appropriate libraries. - Train the classifier using the training set and tune the hyperparameters using the validation set. - Evaluate the trained classifier on the test set using the following metrics: Accuracy: The overall classification accuracy. Precision: The ability to correctly classify positive samples. Recall: The ability to correctly identify positive samples. F1-score: The harmonic mean of precision and recall. Confusion Matrix: A table showing the number of correct and incorrect predictions for each class. Report: Prepare a one-page report summarizing the results of your scene classification experiment and the architecture of your network. Submit your Python code along with the one-page report summarizing the results. Make sure to organize your code and report in a clear and readable manner.

2023-06-09 上传
3. Choose a suitable deep-learning algorithm for scene classification. Convolutional Neural Networks (CNNs) are a popular choice for image classification tasks like this. 4. Implement the chosen ...

ERROR: No matching distribution found for tensorflow.contrib.rnn.python.ops

2023-03-29 上传
As of TensorFlow 2.0, the `tensorflow.contrib` ... It then trains the model for 10 epochs on the training data `x_train` and `y_train`, and validates the model on the test data `x_test` and `y_test`.

from keras.models import Sequential报错：AttributeError: module 'tensorflow.compat.v2' has no attribute '__internal__'

2024-06-07 上传
2. 如果你使用的是旧版本的tensorflow（比如tensorflow 1.x），那么可以尝试升级到tensorflow 2.0或以上版本，或者使用旧版本的keras中的兼容性导入方式`from keras.models import Sequential`。

how to apply cnn, bilstm, attention to predict one feature based on other features. Assume that I want to predict the hourly data of the target feature from 6 am to 18 pm of tomorrow, and I know the hourly data of other features for tomorrow, and I also have historical data of the target feature and other features for training the model. Then, how to apply cnn-bilstm-attention in python

2023-06-06 上传
You can use libraries like TensorFlow or PyTorch for model training. 5. Model Evaluation: Evaluate the model performance using metrics like Mean Squared Error (MSE) or Root Mean Squared Error (RMSE)...

class GuidedBackpropReLUModel: def init(self, model, use_cuda): self.model = model self.model.eval() self.cuda = use_cuda if self.cuda: self.model = model.cuda() for idx, module in self.model.features._modules.items(): if module.class.name == 'ReLU': self.model.features._modules[idx] = GuidedBackpropReLU() def forward(self, input): model1 = nn.Sequential(*list(self.model.children())[1:]) return model1(input) def forward_static(self, input): if self.cuda: output = self.forward(input.cuda()) else: output = self.forward(input) return output def call(self, input, index=None): output = self.forward_static(input) if index is None: index = np.argmax(output.cpu().data.numpy()) one_hot = np.zeros((1, output.size()[-1]), dtype=np.float32) one_hot[0][index] = 1 one_hot = Variable(torch.from_numpy(one_hot), requires_grad=True) if self.cuda: one_hot = torch.sum(one_hot.cuda() * output) else: one_hot = torch.sum(one_hot * output) one_hot.backward() output = input.grad.cpu().data.numpy() output = output[0, :, :, :] return output 代码中input.gard为空怎么解决，代码如何修改

2023-06-03 上传
for idx, module in self.model.features._modules.items(): if module.__class__.__name__ == 'ReLU': self.model.features._modules[idx] = GuidedBackpropReLU() def forward(self, input): model1 = nn....
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