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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

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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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Google Earth Engine ——澳大利亚土壤和景观网格 (SLGA) 是澳大利亚土壤属性的综合数据集（CSIROSLGA）

limit at depth 0-5cm % AWC_000_005_95 The soil attribute's 95th percentile confidence limit at depth...limit at depth 5-15cm % AWC_005_015_95 The soil attribute's 95th percentile confidence limit at depth...limit at depth 0-5cm g/cm^3 BDW_000_005_95 The soil attribute's 95th percentile confidence limit at...limit at depth 5-15cm g/cm^3 BDW_005_015_95 The soil attribute's 95th percentile confidence limit at...limit at depth 60-100cm g/cm^3 BDW_060_100_95 The soil attribute's 95th percentile confidence limit

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Landsat 8 Landsat8 Collection2表面反射率数据

: no confidence level set or Low Confidence1: High confidence cirrusBit 3: Cloud 0: Cloud confidence...: High confidence cloud shadowBit 5: Snow 0: Snow/Ice Confidence is not high1: High confidence...confidence level set or Low Confidence 1: High confidence cirrus Bit 3: Cloud 0: Cloud confidence...00: No confidence level set 01: Low confidence 10: Medium confidence 11: High confidence Bits...: High confidence Bits 12-13: Snow/Ice Confidence 00: No confidence level set 01: Low confidence

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Landsat 8 Landsat8 Collection2大气层顶反射率数据

66 percent confidence)3: High, (67-100 percent confidence)Bits 7-8: Cloud Shadow Confidence 0:...confidence)3: High, (67-100 percent confidence)Bits 9-10: Snow / Ice Confidence 0: Not Determined.../ Condition does not exist.1: Low, (0-33 percent confidence)2: Medium, (34-66 percent confidence)3:...34-66 percent confidence) 3: High, (67-100 percent confidence) Bits 7-8: Cloud Shadow Confidence...confidence) 3: High, (67-100 percent confidence) Bits 9-10: Snow / Ice Confidence 0: Not Determined

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GEE错误：Image.select: Band pattern ‘BQA‘ did not match any bands. Available bands:

: no confidence level set or Low Confidence 1: High confidence cirrus Bit 3: Cloud 0: Cloud confidence...1: High confidence cloud shadow Bit 5: Snow 0: Snow/Ice Confidence is not high 1: High confidence...0: No confidence level set 1: Low confidence 2: Medium confidence 3: High confidence Bits 10-11:...Cloud Shadow Confidence 0: No confidence level set 1: Low confidence 2: Reserved 3: High confidence...confidence Bits 14-15: Cirrus Confidence 0: No confidence level set 1: Low confidence 2: Reserved

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非极大值抑制(Non-Maximum Suppression)

Demo如下图： [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。...score index = order[-1] # Pick the bounding box with largest confidence score...score for (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..., end_x, end_y), confidence in zip(picked_boxes, picked_score): (w, h), baseline = cv2.getTextSize

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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。...score index = order[-1] # Pick the bounding box with largest confidence score...score for (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..., end_x, end_y), confidence in zip(picked_boxes, picked_score): (w, h), baseline = cv2.getTextSize

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opencv--基于深度学习的人脸检测器

(i.e., probability) associated with the # prediction confidence = detections[0, 0, i, 2] # filter...out weak detections by ensuring the `confidence` is # greater than the minimum confidence if confidence...> args["confidence"]: # compute the (x, y)-coordinates of the bounding box for the # object box...out weak detections by ensuring the `confidence` is # greater than the minimum confidence if confidence...confidence"]: continue # compute the (x, y)-coordinates of the bounding box for the

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python数据挖掘 pycaret.arules 关联规则学习

(A=>B)= number of A and B/number 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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关于《Python数据挖掘入门与实战》读书笔记二（亲和性分析）

#输出某两件商品的支持度和置信度 def print_especial_rule(premise,conclusion,support,confidence,features):..., features) #输出该结果集置信度topN最高的商品 def print_topN_confidence_rule(support,confidence,features,topN...): sorted_confidence = sorted(confidence.items(), key=itemgetter(1), reverse=True) print('置信度最高的前...[index][0] print_especial_rule(premise, conclusion, support, confidence, features) if __...条规则 print_topN_confidence_rule(support, confidence, features, 5)

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Keyman算法设计哲学

= alineCount ∗ SubMethod.Confidence‾(a ϵ(0,1))\mathit{Confidence\ =\ a^{lineCount}\ *\ \overline{SubMethod.Confidence...} \quad \left(a\ \epsilon (0,1)\right)}Confidence = alineCount ∗ SubMethod.Confidence(a ϵ(0,1)) 以下两种情况下...，SubMethod.ConfidenceSubMethod.ConfidenceSubMethod.Confidence视为1：一个函数没有调用子函数时，SubMethod.Confidence‾\...overline{SubMethod.Confidence}SubMethod.Confidence整项视为1 调用的子函数为系统函数 / 第三方库函数时，SubMethod.ConfidenceSubMethod.ConfidenceSubMethod.Confidence...}}Confidence:=(lineCountremainLineCount ∗oldConfidence +lineCountnewLineCount∗anewLineCount ∗newSubMethod.Confidence

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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： ? ? ? ? ?...算法选择： proportions——confidence interval——confidence intervals for one proportion 或 confidence intervals...——proportions——confidence intervals for one proportion 2....【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！...interval type: two sided（双尾） confidence level: 1-α confidence interval width（two sided）：置信区间宽度，即置信区间上限与下限之差

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Google Earth Engine ——LANDSATLT4LT5_L1T_ANNUAL_GREENEST_TOA归一化植被指数（NDVI）值最高的像素数据集

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论文推荐：大型语言模型能自我解释吗?

The confidence should be a decimal number between 0 and 1, with 0 being the lowest confidence and 1 being...the highest confidence....The confidence should be a decimal number between 0 and 1, with 0 being the lowest confidence and 1 being...the highest confidence....the highest confidence.

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Google Earth Engine（GEE）——全球建筑物矢量图免费下载Open Buildings V1 Polygons

>= 0.60 && confidence < 0.65'); var t_065_070 = t.filter('confidence >= 0.65 && confidence confidence >= 0.70'); Map.addLayer(t_060_065, {color: 'FF0000'}, 'Buildings...confidence [0.60; 0.65)'); Map.addLayer(t_065_070, {color: 'FFFF00'}, 'Buildings confidence [0.65; 0.70...)'); Map.addLayer(t_gte_070, {color: '00FF00'}, 'Buildings confidence >= 0.70'); Map.setCenter(3.389,...('confidence >= 0.65 && confidence < 0.70'), color: 'FFFF00' }, { filter: ee.Filter.expression

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Google Earth Engine ——LANDSAT8——RAW系列数据

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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。...score index = order[-1] # Pick the bounding box with largest confidence score...score for (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..., end_x, end_y), confidence in zip(picked_boxes, picked_score): (w, h), baseline = cv2.getTextSize

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基于python的opencv人脸识别（具体人）的模型

y, w, h) in faces: cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2) idnum, confidence...= recognizer.predict(gray[y:y + h, x:x + w]) if confidence confidence = "{0}%".format(round(100 - confidence)) else: idnum = "unknown..." confidence = "{0}%".format(round(100 - confidence)) cv2.putText(img, str(idnum),...(x + 5, y - 5), font, 1, (0, 0, 255), 1) cv2.putText(img, str(confidence), (x + 5, y + h - 5),

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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)...模型参数 support 最小支持度 confidence最小自信度以经典的啤酒尿布为例，看看在R中如何实现关联规则算法 data <- read.transactions( "data.csv...=0.5 ) ) inspect(rules) lhs rhs support confidence lift [1] {} => {啤酒} 0.6 0.60

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Confidence interval and Prediction interval

Knowledge Representation Learning with Confidence

Google Earth Engine ——澳大利亚土壤和景观网格 (SLGA) 是澳大利亚土壤属性的综合数据集（CSIROSLGA）

Landsat 8 Landsat8 Collection2表面反射率数据

Landsat 8 Landsat8 Collection2大气层顶反射率数据

GEE错误：Image.select: Band pattern ‘BQA‘ did not match any bands. Available bands:

非极大值抑制(Non-Maximum Suppression)

非极大值抑制(Non-Maximum Suppression)

opencv--基于深度学习的人脸检测器

python数据挖掘 pycaret.arules 关联规则学习

关于《Python数据挖掘入门与实战》读书笔记二（亲和性分析）

Keyman算法设计哲学

统计学教程：总体率估计样本量估算

Google Earth Engine ——LANDSATLT4LT5_L1T_ANNUAL_GREENEST_TOA归一化植被指数（NDVI）值最高的像素数据集

论文推荐：大型语言模型能自我解释吗?

Google Earth Engine（GEE）——全球建筑物矢量图免费下载Open Buildings V1 Polygons

Google Earth Engine ——LANDSAT8——RAW系列数据

·非极大值抑制解析

基于python的opencv人脸识别（具体人）的模型

R关联规则算法（支持度、自信度、提升度）

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