Classification Algorithm in Machine Learning
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Engineering Mechanics Introduction and its Application
Engineering Mechanics Introduction and its ApplicationSakthivel M Engineering Mechanics Introduction
最新版美国圣莫尼卡学院毕业证（SMC毕业证书）原版定制
最新版美国圣莫尼卡学院毕业证（SMC毕业证书）原版定制Taqyea 鉴于此，定制圣莫尼卡学院学位证书提升履历【q薇1954292140】原版高仿圣莫尼卡学院毕业证(SMC毕业证书)可先看成品样本【q薇1954292140】帮您解决在美国圣莫尼卡学院未毕业难题，美国毕业证购买，美国文凭购买，【q微1954292140】美国文凭购买，美国文凭定制，美国文凭补办。专业在线定制美国大学文凭，定做美国本科文凭，【q微1954292140】复制美国Santa Monica College completion letter。在线快速补办美国本科毕业证、硕士文凭证书，购买美国学位证、圣莫尼卡学院Offer，美国大学文凭在线购买。
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4th International Conference on Computer Science and Information Technology (...
4th International Conference on Computer Science and Information Technology (...ijait 4th International Conference on Computer Science and Information Technology
(COMSCI 2025) will act as a major forum for the presentation of innovative ideas,
approaches, developments, and research projects in the area computer Science and
Information Technology. It will also serve to facilitate the exchange of information
between researchers and industry professionals to discuss the latest issues and
advancement in the research area.
The basics of hydrogenation of co2 reaction
The basics of hydrogenation of co2 reactionkumarrahul230759 basics of hydrogenation of CO2 where syntheis and literature review is well explained
社内勉強会資料_Chain of Thought .
社内勉強会資料_Chain of Thought .NABLAS株式会社 本資料「To CoT or not to CoT?」では、大規模言語モデルにおけるChain of Thought（CoT）プロンプトの効果について詳しく解説しています。
CoTはあらゆるタスクに効く万能な手法ではなく、特に数学的・論理的・アルゴリズム的な推論を伴う課題で高い効果を発揮することが実験から示されています。
一方で、常識や一般知識を問う問題に対しては効果が限定的であることも明らかになりました。
複雑な問題を段階的に分解・実行する「計画と実行」のプロセスにおいて、CoTの強みが活かされる点も注目ポイントです。
This presentation explores when Chain of Thought (CoT) prompting is truly effective in large language models.
The findings show that CoT significantly improves performance on tasks involving mathematical or logical reasoning, while its impact is limited on general knowledge or commonsense tasks.
Deep Learning for Natural Language Processing_FDP on 16 June 2025 MITS.pptx
Deep Learning for Natural Language Processing_FDP on 16 June 2025 MITS.pptxresming1 This gives an introduction to how NLP has evolved from the time of World War II till this date through the advances in approaches, architectures and word representations. From rule based approaches, it advanced to statistical approaches. from traditional machine learning algorithms it advanced to deep neural network architectures. Deep neural architectures include recurrent neural networks, long short term memory, gated recurrent units, seq2seq models, encoder decoder models, transformer architecture, upto large language models and vision language models which are multimodal in nature.
Development of Portable Biomass Briquetting Machine (S, A & D)-1.pptx
Development of Portable Biomass Briquetting Machine (S, A & D)-1.pptxaniket862935 Biomass briquetting Machine
3. What is the principles of Teamwork_Module_V1.0.ppt
3. What is the principles of Teamwork_Module_V1.0.pptengaash9 Demonstrate the role of teamwork in the execution of systems engineering.
Describe the principles of successful teams.
20CE601- DESIGN OF STEEL STRUCTURES ,INTRODUCTION AND ALLOWABLE STRESS DESIGN
20CE601- DESIGN OF STEEL STRUCTURES ,INTRODUCTION AND ALLOWABLE STRESS DESIGNgowthamvicky1 • Understand concepts of limit state and working stress method of design of structural steel members and various types of connections.
• Determine net area and effective sections in tension members, tension splices, lug angles and gussets.
• Execute design of compression members as per IS codal practice.
• Analyze concepts of design of flexural members.
• Design structural systems such as roof trusses, gantry girders as per provisions of IS 800 – 2007 of practice for limit state method.
OUTCOMES:
On successful completion of this course, the students will be able to,
• Analyze different types of bolted and welded connections.
• Develop skills to design tension members, splices, lug angles and gussets.
• Elaborate IS Code design practice of various compression members.
• Design laterally supported and unsupported beams, built-up beams, plate girders and stiffeners.
• Acquire knowledge about components of industrial structures, Gantry girders and roof trusses.
TEXT BOOKS:
1. Bhavikatti S S, “Design of Steel Structures”, By Limit State Method as per IS: 800 – 2007, IK International Publishing House Pvt. Ltd., 2019.
2. Subramanian N, “Design of Steel Structures”, Oxford University Press 2011.
REFERENCE BOOKS:
1. Duggal S K, “Limit State Design of Steel Structures”, Tata, McGraw Hill Education Pvt. Ltd., New Delhi, 2017.
2. Shiyekar M R, “Limit State Design in Structural Steel”, PHI Learning Private Limited, New Delhi, 2013.
3. IS: 800 – 2007, IS: 800 – 1984, General Construction in Steel – Code of Practice, BIS, New Delhi.
Structural steel types – Mechanical Properties of structural steel- Indian structural steel products- Steps involved in the Deign Process -Steel Structural systems and their Elements- -Type of Loads on Structures and Load combinations- Code of practices, Loading standards and Specifications - Concept of Allowable Stress Method, and Limit State Design Methods for Steel structures-Relative advantages and Limitations-Strengths and Serviceability Limit states.
Allowable stresses as per IS 800 section 11 -Concepts of Allowable stress design for bending and Shear –Check for Elastic deflection-Calculation of moment carrying capacity –Design of Laterally supported Solid Hot Rolled section beams-Allowable stress deign of Angle Tension and Compression Members and estimation of axial load carrying capacity.
Type of Fasteners- Bolts Pins and welds- Types of simple bolted and welded connections Relative advantages and Limitations-Modes of failure-the concept of Shear lag-efficiency of joints- Axially loaded bolted connections for Plates and Angle Members using bearing type bolts –Prying forces and Hanger connection– Design of Slip critical connections with High strength Friction Grip bolts.- Design of joints for combined shear and Tension- Eccentrically Loaded Bolted Bracket Connections- Welds-symbols and specifications- Effective area of welds-Fillet and but Welded connections-Axially Loaded connections for Plate and angle truss members and
Water demand - Types , variations and WDS
Water demand - Types , variations and WDSdhanashree78 Water demand refers to the volume of water needed or requested by users for various purposes. It encompasses the water required for domestic, industrial, agricultural, public, and other uses. Essentially, it represents the overall need or quantity of water required to meet the demands of different sectors and activities.
Cadastral Maps
Cadastral MapsGoogle Preparation of cadastral maps based by Engineer Dungo Tizazu from Dire Dawa University
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Classification Algorithm in Machine Learning
- 1. Supervised vs. Unsupervised Learning Supervised learning (classification) ◦ Supervision: The training data (observations, measurements, etc.) are accompanied by labels indicating the class of the observations ◦ New data is classified based on the training set Unsupervised learning (clustering) ◦ The class labels of training data is unknown ◦ Given a set of measurements, observations, etc. with the aim of establishing the existence of classes or clusters in the data
- 4. Classification predicts categorical class labels (discrete or nominal) classifies data (constructs a model) based on the training set and the values (class labels) in a classifying attribute and uses it in classifying new data Prediction models continuous-valued functions, i.e., predicts unknown or missing values Typical applications Credit approval Target marketing Medical diagnosis Fraud detection Classification vs. Prediction
- 5. Classification: Definition Given a collection of records (training set ) Each record contains a set of attributes, one of the attributes is the class. Find a model for class attribute as a function of the values of other attributes. Goal: previously unseen records should be assigned a class as accurately as possible. A test set is used to determine the accuracy of the model. Usually, the given data set is divided into training and test sets, with training set used to build the model and test set used to validate it.
- 6. Classification—A Two-Step Process Model construction: describing a set of predetermined classes Each tuple/sample is assumed to belong to a predefined class, as determined by the class label attribute The set of tuples used for model construction is training set The model is represented as classification rules, decision trees, or mathematical formulae
- 7. Classification—A Two-Step Process Model usage: for classifying future or unknown objects Estimate accuracy of the model The known label of test sample is compared with the classified result from the model Accuracy rate is the percentage of test set samples that are correctly classified by the model Test set is independent of training set, otherwise over-fitting will occur If the accuracy is acceptable, use the model to classify data tuples whose class labels are not known
- 8. Classification Process (1): Model Construction Training Data NAME RANK YEARS TENURED Mike Assistant Prof 3 no Mary Assistant Prof 7 yes Bill Professor 2 yes Jim Associate Prof 7 yes Dave Assistant Prof 6 no Anne Associate Prof 3 no Classification Algorithms IF rank = ‘professor’ OR years > 6 THEN tenured = ‘yes’ Classifier (Model)
- 9. Classification Process (2): Use the Model in Prediction Classifier Testing Data NAME RANK YEARS TENURED Tom Assistant Prof 2 no Merlisa Associate Prof 7 no George Professor 5 yes Joseph Assistant Prof 7 yes Unseen Data (Jeff, Professor, 4) Tenured?
- 10. The Learning Process in spam mail Example Email Server ● Number of recipients ● Size of message ● Number of attachments ● Number of "re's" in the subject line … Model Learning Model Testin g
- 11. An Example A fish-packing plant wants to automate the process of sorting incoming fish according to species As a pilot project, it is decided to try to separate sea bass from salmon using optical sensing Classification
- 12. An Example (continued) Features/attributes: Length Lightness Width Position of mouth Classification
- 13. An Example (continued) Preprocessing: Images of different fishes are isolated from one another and from background; Feature extraction: The information of a single fish is then sent to a feature extractor, that measure certain “features” or “properties”; Classification: The values of these features are passed to a classifier that evaluates the evidence presented, and build a model to discriminate between the two species Classification
- 14. An Example (continued) Classification Domain knowledge: ◦ A sea bass is generally longer than a salmon Related feature: (or attribute) ◦ Length Training the classifier: ◦ Some examples are provided to the classifier in this form: <fish_length, fish_name> ◦ These examples are called training examples ◦ The classifier learns itself from the training examples, how to distinguish Salmon from Bass based on the fish_length
- 15. An Example (continued) Classification Classification model (hypothesis): ◦ The classifier generates a model from the training data to classify future examples (test examples) ◦ An example of the model is a rule like this: ◦ If Length >= l* then sea bass otherwise salmon ◦ Here the value of l* determined by the classifier Testing the model ◦ Once we get a model out of the classifier, we may use the classifier to test future examples ◦ The test data is provided in the form <fish_length> ◦ The classifier outputs <fish_type> by checking fish_length against the model
- 16. An Example (continued) So the overall classification process goes like this Classification Preprocessing, and feature extraction Training Training Data Model Test/Unlabeled Data Testing against model/ Classification Feature vector Preprocessing, and feature extraction Feature vector Prediction/ Evaluation
- 17. An Example (continued) Classification Pre- processing, Feature extraction 12, salmon 15, sea bass 8, salmon 5, sea bass Training data Feature vector Training If len > 12, then sea bass else salmon Model Test data 15, salmon 10, salmon 18, ? 8, ? Feature vector Test/ Classify sea bass (error!) salmon (correct) sea bass salmon Evaluation/Prediction Pre- processing, Feature extraction Labeled data Unlabeled data
- 18. An Example (continued) Classification Why error? Insufficient training data Too few features Too many/irrelevant features Overfitting / specialization
- 19. An Example (continued) Classification Pre- processing, Feature extraction 12, 4, salmon 15, 8, sea bass 8, 2, salmon 5, 10, sea bass Training data Feature vector Training If ltns > 6 or len*5+ltns*2>100 then sea bass else salmon Model Test data 15, 2, salmon 10, 7, salmon 18, 7, ? 8, 5, ? Feature vector Test/ Classify salmon (correct) salmon (correct) sea bass salmon Evaluation/Prediction Pre- processing, Feature extraction
- 20. Linear, Non-linear, Multi-class and Multi-label classification
- 21. Linear Classification A linear classifier achieves this by making a classification decision based on the value of a linear combination of the characteristics. A classification algorithm (Classifier) that makes its classification based on a linear predictor function combining a set of weights with the feature vector Decision boundaries is flat ◦ Line, plane, …. May involve non-linear operations
- 24. Linear Classifiers How would you classify this data? New Recipients Email Length
- 27. Linear Classifiers Any of these would be fine.. ..but which is best? New Recipients Email Length
- 28. Classifier Margin New Recipients Define the margin of a linear classifier as the width that the boundary could be increased by before hitting a datapoint. Email Length
- 34. No Linear Classifier can cover all instances How would you classify this data? New Recipients Email Length
- 35. • Ideally, the best decision boundary should be the one which provides an optimal performance such as in the following figure
- 36. No Linear Classifier can cover all instances Email Length New Recipients
- 37. What is multiclass Output ◦ In some cases, output space can be very large (i.e., K is very large) Each input belongs to exactly one class (c.f. in multilabel, input belongs to many classes)
- 38. Multi-Classes Classification Multi-class classification is simply classifying objects into any one of multiple categories. Such as classifying just into either a dog or cat from the dataset. 1.When there are more than two categories in which the images can be classified, and 2.An image does not belong to more than one class If both of the above conditions are satisfied, it is referred to as a multi- class image classification problem
- 40. Multi-label classification When we can classify an image into more than one class (as in the image beside), it is known as a multi-label image classification problem. Multi-label classification is a type of classification in which an object can be categorized into more than one class. For example, In the image dataset, we will classify a picture as the image of a dog or cat and also classify the same image based on the breed of the dog or cat . These are all labels of the given images. Each image here belongs to more than one class and hence it is a multi-label image classification problem.
- 43. Multi classVs multi label classification