the model 200 300 classifier machine were sent to the usa

the model 200 300 classifier machine were sent to the usa

<h3>Some Empirical Results on Two Spam Detection Methods</h3><p>the model fit to the nonlinearly separable data. The soft margin SVMs behave like hardmargin SVMs if the param eter C is large enough. See [5, 8,9, 131 for details. 2.3. Naive Bayes Classifier The naive Bayes algorithm is among the most effective probabilistic approaches for learning to classify text docu ments [71. </p>

Some Empirical Results on Two Spam Detection Methods

the model fit to the nonlinearly separable data. The soft margin SVMs behave like hardmargin SVMs if the param eter C is large enough. See [5, 8,9, 131 for details. 2.3. Naive Bayes Classifier The naive Bayes algorithm is among the most effective probabilistic approaches for learning to classify text docu ments [71.

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<h3>HEAVY PARTICLE CONCENTRATION TECHNOLOGY  acmine.com</h3><p>more Model5 units were built. All ten units were sold within 2 weeks of completion. A more advanced version of the machine was built in 1991 and in 1994 two large Model200 machines were built and sold to a mine in B.C., where they operated successfully, processing more than 180 cubic yards per hour. </p>

HEAVY PARTICLE CONCENTRATION TECHNOLOGY acmine.com

more Model5 units were built. All ten units were sold within 2 weeks of completion. A more advanced version of the machine was built in 1991 and in 1994 two large Model200 machines were built and sold to a mine in B.C., where they operated successfully, processing more than 180 cubic yards per hour.

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<h3>About this release  McAfee Data Loss Prevention 10.0.200</h3><p>Model 4400 Model 5500 Endpoint computers RAM  1 GB minimum (2 GB recommended) Hard disk  300 MB minimum free disk space (500 MB recommended) Network 100 megabit LAN serving all workstations and the McAfee ePO server </p>

About this release McAfee Data Loss Prevention 10.0.200

Model 4400 Model 5500 Endpoint computers RAM 1 GB minimum (2 GB recommended) Hard disk 300 MB minimum free disk space (500 MB recommended) Network 100 megabit LAN serving all workstations and the McAfee ePO server

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<h3>Interlabeler and intralabeler variability of condition </h3><p>Each fold contains three elements: 1) an initial set of six conditions that are used to induce the initial classification model, 2) a test set of 200 conditions on which the induced classifier is tested and evaluated in each active learning iteration, and 3) a pool of 310 unlabeled conditions from which the conditions are selected to be labeled  </p>

Interlabeler and intralabeler variability of condition

Each fold contains three elements: 1) an initial set of six conditions that are used to induce the initial classification model, 2) a test set of 200 conditions on which the induced classifier is tested and evaluated in each active learning iteration, and 3) a pool of 310 unlabeled conditions from which the conditions are selected to be labeled

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<h3>Text Classification and Sentiment Analysis  Ahmet Taspinar</h3><p>bagofwords modelClassifier evaluation  Here we need to remember that this is a supervised machine learning algorithm: we can estimate the priorprobabilities  </p>

Text Classification and Sentiment Analysis Ahmet Taspinar

bagofwords modelClassifier evaluation Here we need to remember that this is a supervised machine learning algorithm: we can estimate the priorprobabilities

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<h3>100 Data Science Interview Questions and Answers (General </h3><p>In collaboration with data scientists, industry experts and top counsellors, we have put together a list of general data science interview questions and answers to help you with your preparation in applying for data science jobs. </p>

100 Data Science Interview Questions and Answers (General

In collaboration with data scientists, industry experts and top counsellors, we have put together a list of general data science interview questions and answers to help you with your preparation in applying for data science jobs.

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<h3>Naïve Bayes using R  BI Corner</h3><p>In the previous example you were given a recipe which allowed you to construct a Naïve Bayes classifier. This was for a case where we had continuous predictor variables. In this question you have to work out what the parameters of a Naïve Bayes model should be for some discrete data. </p>

Naïve Bayes using R BI Corner

In the previous example you were given a recipe which allowed you to construct a Naïve Bayes classifier. This was for a case where we had continuous predictor variables. In this question you have to work out what the parameters of a Naïve Bayes model should be for some discrete data.

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<h3>Active learning for automatic classification of software </h3><p>The two sets of classifiers were initialized with 300 150 0.047 0.0300 0.042 to 0.052 ve descriptives 350 150 0.015 0.0105 0.013 to 0.017 the same training set at size 100. t Size: 100, 150, 200, 250, 300, 350 As the training set size increases, so does the gain in Clas sifier Precision of bootstrapping over batch learning. </p>

Active learning for automatic classification of software

The two sets of classifiers were initialized with 300 150 0.047 0.0300 0.042 to 0.052 ve descriptives 350 150 0.015 0.0105 0.013 to 0.017 the same training set at size 100. t Size: 100, 150, 200, 250, 300, 350 As the training set size increases, so does the gain in Clas sifier Precision of bootstrapping over batch learning.

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<h3>Using statistical and machine learning to help institutions </h3><p>Selected events were marked as either suspicious or appropriate by privacy officers, and served as the gold standard set for model evaluation. The authors trained logistic regression (LR) and support vector machine (SVM) models on 10fold crossvalidation sets of 1291 labeled events. </p>

Using statistical and machine learning to help institutions

Selected events were marked as either suspicious or appropriate by privacy officers, and served as the gold standard set for model evaluation. The authors trained logistic regression (LR) and support vector machine (SVM) models on 10fold crossvalidation sets of 1291 labeled events.

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<h3>Ground Search Metal Detectors eBay</h3><p>Has NEL Attack, Garrett puck, Garrett stock coil and the coil that comes with the pro. Regular Garrett headphones and Garrett waterproof headphones. Everything works perfect. No issues. All coils were tested under water and are all good to go. Machine confirmed waterproof as well. Selling to fund my guitar hobby. Original box included as well. </p>

Ground Search Metal Detectors eBay

Has NEL Attack, Garrett puck, Garrett stock coil and the coil that comes with the pro. Regular Garrett headphones and Garrett waterproof headphones. Everything works perfect. No issues. All coils were tested under water and are all good to go. Machine confirmed waterproof as well. Selling to fund my guitar hobby. Original box included as well.

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<h3>Machine LearningBased Differential Network Analysis: A Study </h3><p>Machine learning ([ML][1]) is an intelligent data mining technique that builds a prediction model based on the learning of prior knowledge to recognize patterns in largescale data sets. </p>

Machine LearningBased Differential Network Analysis: A Study

Machine learning ([ML][1]) is an intelligent data mining technique that builds a prediction model based on the learning of prior knowledge to recognize patterns in largescale data sets.

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<h3>Machine Learning authors/titles "new.LG" arXiv</h3><p>In recent years, deep learning researchers have focused on how to find the interpretability behind deep learning models. However, today cognitive competence of human has not completely covered the deep learning model. In other words, there is a gap between the deep learning model and the cognitive mode. </p>

Machine Learning authors/titles "new.LG" arXiv

In recent years, deep learning researchers have focused on how to find the interpretability behind deep learning models. However, today cognitive competence of human has not completely covered the deep learning model. In other words, there is a gap between the deep learning model and the cognitive mode.

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<h3>A Machine Learning and CrossValidation Approach for the </h3><p>To analyze effect of the ground truth data size on the accuracy, the ground truth data sets of size 300 available in the research for each physiognomic class were randomly sampled into 12 sets: 25, 50, 75, 100, 125, 150, 175, 200, 225, 250, 275, and 300. </p>3

A Machine Learning and CrossValidation Approach for the

To analyze effect of the ground truth data size on the accuracy, the ground truth data sets of size 300 available in the research for each physiognomic class were randomly sampled into 12 sets: 25, 50, 75, 100, 125, 150, 175, 200, 225, 250, 275, and 300.

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<h3>Fraud, waste, and abuse in entitlement programs Deloitte </h3><p>Fraud, waste, and abuse in government benefits programs drain billions of taxpayer dollars.  Hoover to organize 300 men and women to seek waste in what Hoover  </p>

Fraud, waste, and abuse in entitlement programs Deloitte

Fraud, waste, and abuse in government benefits programs drain billions of taxpayer dollars. Hoover to organize 300 men and women to seek waste in what Hoover

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<h3>Bootstrap Methods for Foreign Currency Exchange Rates</h3><p>0 100 200 300 400 500 600 700 800.  During the 1990s many methods were proposed for combining multiple classifiers for a single recognition task.  Unlike the traditional single model  </p>

Bootstrap Methods for Foreign Currency Exchange Rates

0 100 200 300 400 500 600 700 800. During the 1990s many methods were proposed for combining multiple classifiers for a single recognition task. Unlike the traditional single model

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<h3>Nested Machine Learning Facilitates Increased Sequence </h3><p>Raw melt data files were exported and sent for further analysis utilizing the support vector machine learning algorithm.  (typically 300500 bp long) were used to choose the best matching  </p>

Nested Machine Learning Facilitates Increased Sequence

Raw melt data files were exported and sent for further analysis utilizing the support vector machine learning algorithm. (typically 300500 bp long) were used to choose the best matching

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<h3>Filtered selection coupled with support vector machines </h3><p>300, and 500 genes most relevant to CRC using the minimumredundancymaximumrelevance (mRMR) technique. With these gene sets, an SVM model was designed using four different </p>

Filtered selection coupled with support vector machines

300, and 500 genes most relevant to CRC using the minimumredundancymaximumrelevance (mRMR) technique. With these gene sets, an SVM model was designed using four different

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<h3>Finding scientific topics PNAS</h3><p>A first step in identifying the content of a document is determining which topics that document addresses. We describe a generative model for documents, introduced by Blei, Ng, and Jordan [Blei, D. M., Ng, A. Y. &ampJordan, M. I. (2003) J. Machine Learn. </p>

Finding scientific topics PNAS

A first step in identifying the content of a document is determining which topics that document addresses. We describe a generative model for documents, introduced by Blei, Ng, and Jordan [Blei, D. M., Ng, A. Y. &Jordan, M. I. (2003) J. Machine Learn.

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<h3>Evaluating associative classification algorithms for Big Data </h3><p>Unlike these approaches, in AC the training phase is about searching for hidden knowledge by means of association rule mining algorithms and then a classification model (classifier) is constructed after sorting the knowledge in regards to certain criteria as well as pruning useless and redundant knowledge . </p>

Evaluating associative classification algorithms for Big Data

Unlike these approaches, in AC the training phase is about searching for hidden knowledge by means of association rule mining algorithms and then a classification model (classifier) is constructed after sorting the knowledge in regards to certain criteria as well as pruning useless and redundant knowledge .

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<h3>Metal Detector Accessories eBay</h3><p>The RAPTOR handles were designed with the serious beach hunter in mind!  Trending at $6.95 eBay determines this price through a machine learned model of the  </p>

Metal Detector Accessories eBay

The RAPTOR handles were designed with the serious beach hunter in mind! Trending at $6.95 eBay determines this price through a machine learned model of the

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<h3>Digital Image Processing  California Institute of Technology</h3><p>All the revisions just mentioned resulted in over 400 new images, over 200 new line drawings and tables, and more than 80 new homework problems. Where appropriate, complex processing procedures were summarized in the form of stepbystep algorithm formats.The references at the end of all chapters were updated also. </p>

Digital Image Processing California Institute of Technology

All the revisions just mentioned resulted in over 400 new images, over 200 new line drawings and tables, and more than 80 new homework problems. Where appropriate, complex processing procedures were summarized in the form of stepbystep algorithm formats.The references at the end of all chapters were updated also.

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<h3>ru v product mobile spiral classifier  aelabworld.co.za</h3><p>Mtw Milling Machine Spiral Classifier Ball Mill. vibrating screen, jigging machine, ball mill, classifier, Live ChatSpiral Classifier, Grinding Mill, Crusher Machine, Sand . Spiral Classifier. Spiral classifier is widely used in milling grade ore concentrator circuit and washing, MTW Milling Machine SCM Ultrafine Mill Ball Mill. </p>

ru v product mobile spiral classifier aelabworld.co.za

Mtw Milling Machine Spiral Classifier Ball Mill. vibrating screen, jigging machine, ball mill, classifier, Live ChatSpiral Classifier, Grinding Mill, Crusher Machine, Sand . Spiral Classifier. Spiral classifier is widely used in milling grade ore concentrator circuit and washing, MTW Milling Machine SCM Ultrafine Mill Ball Mill.

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<h3>Artificial neural network  </h3><p>Other neural network computational machines were  An artificial neural network is a network of  such as support vector machine classifier or a multi  </p>

Artificial neural network

Other neural network computational machines were An artificial neural network is a network of such as support vector machine classifier or a multi

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<h3>Feature Representation Using Deep Autoencoder for Lung Nodule </h3><p>Second, classifier design constructs classifier based on supervised or unsupervised machine learning method. However, these methods belong to the fields of traditional image processing and machine learning, which can only characterize the abstraction of lung nodule image in a shallow layer and make the research at low level. </p>

Feature Representation Using Deep Autoencoder for Lung Nodule

Second, classifier design constructs classifier based on supervised or unsupervised machine learning method. However, these methods belong to the fields of traditional image processing and machine learning, which can only characterize the abstraction of lung nodule image in a shallow layer and make the research at low level.

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<h3>Continuous LocomotionMode Identification for Prosthetic Legs </h3><p>Continuous LocomotionMode Identification for Prosthetic Legs Based on NeuromuscularMechanical Fusion  is sent to a phasedependent classifier,  kernel were  </p>3

Continuous LocomotionMode Identification for Prosthetic Legs

Continuous LocomotionMode Identification for Prosthetic Legs Based on NeuromuscularMechanical Fusion is sent to a phasedependent classifier, kernel were

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<h3>Machine learning for identifying Randomized Controlled Trials </h3><p>An SVM classifier was built, and citations that were predicted to have <10% probability of being an RCT are discarded. An internal evaluation found that the classifier enable 40% of citations to be discarded from the set sent to the crowd while maintaining 99.9% sensitivity. </p>

Machine learning for identifying Randomized Controlled Trials

An SVM classifier was built, and citations that were predicted to have <10% probability of being an RCT are discarded. An internal evaluation found that the classifier enable 40% of citations to be discarded from the set sent to the crowd while maintaining 99.9% sensitivity.

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<h3>Exploratory study examining the athome feasibility of a </h3><p>The five families that were excluded by the research team due to noncompliance with the required use of the device were also all male, with an average age of 8 years 6 months (SD = 4.04 years  </p>

Exploratory study examining the athome feasibility of a

The five families that were excluded by the research team due to noncompliance with the required use of the device were also all male, with an average age of 8 years 6 months (SD = 4.04 years

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<h3>Watson (computer)  </h3><p>Tweets were passed through a Watson tone analyzer and then sent back to a small computer inside the waist of the dress. As social media is an integral part of their business, the Marchesa team loved how Watson could incorporate that information into the glamour of the gown. </p>

Watson (computer)

Tweets were passed through a Watson tone analyzer and then sent back to a small computer inside the waist of the dress. As social media is an integral part of their business, the Marchesa team loved how Watson could incorporate that information into the glamour of the gown.

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<h3>Computational Design of EpitopeSpecific Functional </h3><p>We thus trained a machine learning model,  Nearly 200 designed antibodies were experimentally tested, yielding three low affinity binders that were then optimized  </p>

Computational Design of EpitopeSpecific Functional

We thus trained a machine learning model, Nearly 200 designed antibodies were experimentally tested, yielding three low affinity binders that were then optimized

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<h3>SPONGY (SPam ONtoloGY): Email   PubMed Central (PMC)</h3><p>Total of 2108 emails were used as a training dataset. 47.8% of dataset were spam and 52.2% were legitimate email. The C4.5 classifier was used to classify the dataset in Weka explorer. 91.51% of emails were classified correctly and 8.49% were classified incorrectly. In the case of spam, precision was 0.872, recall was 0.963. </p>

SPONGY (SPam ONtoloGY): Email PubMed Central (PMC)

Total of 2108 emails were used as a training dataset. 47.8% of dataset were spam and 52.2% were legitimate email. The C4.5 classifier was used to classify the dataset in Weka explorer. 91.51% of emails were classified correctly and 8.49% were classified incorrectly. In the case of spam, precision was 0.872, recall was 0.963.

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