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Confidence interval and Prediction interval

置信区间估计(confidence interval estimate):利用估计的回归方程,对于自变量 x 的一个给定值 x0 ,求出因变量 y 的平均值的估计区间; 预测区间估计(prediction

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Knowledge Representation Learning with Confidence

为了解决这一问题,我们提出了一个新的置信度感知(confidence-aware)知识表示学习框架(CKRL),该框架在识别KGs中可能存在的噪声的同时进行有置信度的知识表示学习。

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    非极大值抑制(Non-Maximum Suppression)

    左图是人脸检测的候选框结果,每个边界框有一个置信度得分(confidence score),如果不使用非极大值抑制,就会有多个候选框出现。右图是使用非极大值抑制之后的结果,符合我们人脸检测的预期结果。 scorefor (start_x, start_y, end_x, end_y), confidence in zip(bounding_boxes, confidence_score): (w, h), baseline = cv2.getTextSize(str(confidence), font, font_scale, thickness) cv2.rectangle(org, (start_x score after non-maximum supressionfor (start_x, start_y, end_x, end_y), confidence in zip(picked_boxes , picked_score): (w, h), baseline = cv2.getTextSize(str(confidence), font, font_scale, thickness) cv2

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    非极大值抑制(Non-Maximum Suppression)

    Demo如下图: 左图是人脸检测的候选框结果,每个边界框有一个置信度得分(confidence score),如果不使用非极大值抑制,就会有多个候选框出现。 scorefor (start_x, start_y, end_x, end_y), confidence in zip(bounding_boxes, confidence_score): (w, h), baseline = cv2.getTextSize(str(confidence), font, font_scale, thickness) cv2.rectangle(org, (start_x score after non-maximum supressionfor (start_x, start_y, end_x, end_y), confidence in zip(picked_boxes , picked_score): (w, h), baseline = cv2.getTextSize(str(confidence), font, font_scale, thickness) cv2

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    python数据挖掘 pycaret.arules 关联规则学习

    (A=>B)= number of A and Bnumber of A,confidence(A=>B)! = confidence(B=>A)  3.lift(A=>B)= confidence(A=>B)support(B),lift(A=>B)= lift(B=>A)对三个准则的解释:  support confidence越高越好,一个高的confidence证明当交易出现了某个antecedent的时候,很大可能会出现某个consequent,也就是某条规则成立的概率越大。   假如confidence(A=>B)=80%,表明如果顾客购买了A,有80%的顾客同时有购买了B。 然而lift只有confidence(A=>B)support(B)= 80% 95% =0.8421,也就是说lift不太支持这条规则成立,因为顾客普遍都会买B,导致了support和confidence

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    ·非极大值抑制解析

    Object Detection左图是人脸检测的候选框结果,每个边界框有一个置信度得分(confidence score),如果不使用非极大值抑制,就会有多个候选框出现。 scorefor (start_x, start_y, end_x, end_y), confidence in zip(bounding_boxes, confidence_score): (w, h), baseline = cv2.getTextSize(str(confidence), font, font_scale, thickness) cv2.rectangle(org, (start_x score after non-maximum supressionfor (start_x, start_y, end_x, end_y), confidence in zip(picked_boxes , picked_score): (w, h), baseline = cv2.getTextSize(str(confidence), font, font_scale, thickness) cv2

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    统计学教程:总体率估计样本量估算

    关于上图中圈出的“Confidence Interval Formula”,有以下几种选择:? 不同选择方式会带来不同的结果,但总体上相差不大:????? 不同Confidence Interval Formula对应结果如下: Exact (Clopper-Pearson):Sample Size(N)=306 Score (Wilson):Sample 算法选择:proportions——confidence interval——confidence intervals for one proportion或 confidence intervals— —proportions——confidence intervals for one proportion2. 【连续校正的二项式的正态近似法】注:help文档中并未对上述几种公式的适用情况做详尽的说明,关于如何选择合适的confidence interval formula,欢迎大家留言讨论!

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    python编码转换实验

    : 1.0, encoding: ascii}>>> chardet.detect(str(b)){confidence: 1.0, encoding: ascii}>>> c = >>> chardet.detect (str(c)){confidence: 1.0, encoding: ascii}>>> print c>>> c.encode(unicode)Traceback (most recent call _highBitDetector.search(aBuf):TypeError: expected string or buffer>>> chardet.detect(d){confidence: 1.0 : 1.0, encoding: ascii}>>> chardet.detect(dd){confidence: 1.0, encoding: ascii}>>> sys.defaultencoding _highBitDetector.search(aBuf):TypeError: expected string or buffer>>> chardet.detect(str(p)){confidence

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    可微分线性带状算法(CS LG)

    上置信界(Upper Confidence Bound,UCB)可以说是线性多臂强盗问题中最常用的方法。 原文题目:Differentiable Linear Bandit Algorithm原文:Upper Confidence Bound (UCB) is arguably the most commonly While conceptually and computationally simple, this method highly relies on the confidence bounds, failing In this work, we aim at learning the confidence bound in a data-driven fashion, making it adaptive to Then, we introduce a gradient estimator, which allows the confidence bound to be learned via gradient

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    R关联规则算法(支持度、自信度、提升度)

    关联规则的强度用支持度(support)和自信度(confidence)来描述,关联规则是否可用,使用提升度(Lift)来描述。 挖掘定义 给定一个数据集,找出其中所有支持度support>=min_support,自信度confidence>=min_confifence的关联规则。 (X->Y)=集合X与集合Y中的项在一条记录中同时出现的次数集合X出现的个数 例如: confidence({啤酒}->{尿布})=啤酒和尿布同时出现的次数啤酒出现的次数提升度(Lift) 度量规则是否可用的指标 ,描述的是相对于不用规则,使用规则可以提高多少,有用的规则的提升度大于1 计算公式=lift({A→B})=confidence({A→B})support(B)实现关联规则的API install.packages (“arules”) apriori(x,parameter=list(support=0.5,confidence=0.5))x 训练样本parameter模型参数support 最小支持度confidence

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    OpenCV+python实现实时目标检测功能

    confidence:过滤弱检测的最小概率阈值,默认值为 20%。 `confidence` is # greater than the minimum confidence if confidence args: # extract the index of the 在 detections 内循环,首先我们提取 confidence 值,confidence = detections。 如果 confidence 高于最低阈值(if confidence args:),那么提取类标签索引(idx = int(detections)),并计算检测到的目标的坐标(box = detections 接着构建一个文本 label,包含 CLASS 名称和 confidence(label = {}: {:.2f}%.format(CLASSES,confidence * 100))。

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    如何使用 OpenCV 编写基于 Node.js 命令行界面和神经网络模型的图像分类

    现在你可以从命令行来执行下述语句:classify --image --filter .filter.txt --confidence 50 CLI 输出所有的 CLI 都有输出因此用户可以理解如何如何来使用它 , typeLabel: {underline value}, description: The minimum confidence level to use for classification = 50 defaultif (confidence in options) { confidence = options.confidence} validate confidenceif (confidence < 0) { console.error(`Negative numbers are not valid for confidence.`) process.exit(1)}if (confidence > 100) { console.error(`A value greater than 100 is not valid for confidence.`) process.exit(1)} confidence

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    项目实践 | 从零开始边缘部署轻量化人脸检测模型——训练篇

    (location) for layer in self.base_net: x = layer(x) for layer in self.extras: x = layer(x) confidence = self.classification_headers(x) confidence = confidence.permute(0, 2, 3, 1).contiguous() confidence location.permute(0, 2, 3, 1).contiguous() location = location.view(location.size(0), -1, 4) return confidence Args: confidence (batch_size, num_priors, num_classes): class predictions. locations (batch_size, num_priors , dim=2) mask = box_utils.hard_negative_mining(loss, labels, self.neg_pos_ratio) confidence = confidence

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    基于OpenCV的视频处理管道

    =0.5): self.confidence = confidence self.net = cv2.dnn.readNetFromCaffe(prototxt, model) def detect(self (i.e., probability) associated with the prediction confidence = detections # filter out weak detections by ensuring the `confidence` is # greater than the minimum confidence if confidence < self.confidence =0.5): self.detector = FaceDetector(prototxt, model, confidence=confidence) self.batch_size = batch_size 我们可以降低设置参数的深度学习模型的置信度confidence 0.2(默认值为0.5)。降低置信度阈值会增加假阳性的发生(在图像中没有脸的位置出现脸)。

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    【教程】OpenCV—Node.js教程系列:Node.js+OpenCV面部脸识别

    { testImages.forEach((img) => { const result = recognizer.predict(img); console.log(predicted: %s, confidence : 1245.68predicted negan to be: negan, confidence: 2247.25predicted rick to be: negan, confidence: 2502.47fisher :predicted daryl to be: daryl, confidence: 452.15predicted negan to be: negan, confidence: 464.76predicted rick to be: rick, confidence: 831.38lbph:predicted daryl to be: daryl, confidence: 108.37predicted negan to be: negan, confidence: 119.33predicted rick to be: rick, confidence: 105.65每个类(角色)仅使用3个图像,我们就可以得到很好的结果

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    置信区间绘图、以10次平均值为例

    np.random.normal(loc=Mu, scale=Sigma, size=(2, data_points))print(data)# predicted expect and calculate confidence high_CI_bound = st.t.interval(0.95, data_points - 1, loc=np.mean(data, 0), scale=st.sem(data)) # plot confidence plt.plot(Mu, color=r, label=grand truth)plt.fill_between(x, low_CI_bound, high_CI_bound, alpha=0.25, label=confidence interval)plt.legend()plt.title(Confidence interval)plt.show()

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    DASI_3 _CI&HT

    intervals & hypothesis tests -> significance & confidence & power ? 简单来说,CI就是我们的confidence internal包括群体参数的概率。CI是关于群体的,不是关于个人的,也不是关于样本的。 如果想要提高accuracy的话,那么需要提高confidence level。但是提升的同时,会带来一些cost。 具体表现在,confidence interval也变高了,引起了precision的降低。trade-offs提高样本大小。 通常,significance level和confidence level是互补的。比如前者5%,后者95%。 两者是否互补为1取决于做的是单尾检定还是双尾检定。?

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    OpenCV DNN模块官方教程(二)YoloV4目标检测实例

    (left, top), FONT_HERSHEY_SIMPLEX, 0.8, Scalar(0, 255, 0), 2);} Remove the bounding boxes with low confidence i) { Scan through all the bounding boxes output from the network and keep only the ones with high confidence j, data += outs.cols) { Mat scores = outs.row(j).colRange(5, outs.cols); Point classIdPoint; double confidence ; Get the value and location of the maximum score minMaxLoc(scores, 0, &confidence, 0, &classIdPoint ); if (confidence > confThreshold) { int centerX = (int)(data * frame.cols); int centerY = (int)(data

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    YOLO

    总得来讲就是让网格负责类别信息,bounding box主要负责坐标信息(部分负责类别信息:confidence也算类别信息)。 每个bounding box除了要回归自身的位置之外,还要附带预测一个confidence值。 这个confidence代表了所预测的box中含有object的置信度和这个box预测的有多准两重信息:confidence = ?。 注意:class信息是针对每个网格的,confidence信息是针对每个bounding box的)?损失函数设计:? )和bounding box预测的confidence信息( ? ) 相乘,就得到每个bounding box的class-specific confidence score。?

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    YOLOV3 基于OpenCV DNN 的目标检测实现

    class general_yolov3(object): def __init__(self, modelpath, is_tiny=False): self.conf_threshold = 0.5 # Confidence 的边界框 img_height, img_width, _ = img_cv2.shape # 只保留高 confidence scores 的输出边界框 # 将最高 score 的类别标签作为边界框的类别标签 class_ids = boxes = class_id = np.argmax(scores) confidence = scores if confidence > self.conf_threshold center_x - width 2) top = int(center_y - height 2) class_ids.append(class_id) confidences.append(float(confidence bottom = result cv2.rectangle(img_cv2, (left, top), (right, bottom), (255, 178, 50), 3) # 边界框的类别名和 confidence

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