scholarly journals USE OF LINEAR ALGEBRA AND PARTIAL DERIVATIVES IN SUPERVISED LEARNING (ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING)

2020 ◽  
Vol 1 (3) ◽  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Supervised Learning ◽  
Linear Algebra ◽  
Partial Derivatives

Graph-Based Semi-Supervised Learning With Big Data

2020 ◽  
pp. 214-244
Author(s):  
Prithish Banerjee ◽  
Mark Vere Culp ◽  
Kenneth Jospeh Ryan ◽  
George Michailidis
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Supervised Learning ◽  
Prior Knowledge ◽  
Linear Algebra ◽  
Real Data ◽  
Data Set ◽  
Regression Problems ◽  
Classification And Regression ◽  
Empirical Demonstration

This chapter presents some popular graph-based semi-supervised approaches. These techniques apply to classification and regression problems and can be extended to big data problems using recently developed anchor graph enhancements. The background necessary for understanding this Chapter includes linear algebra and optimization. No prior knowledge in methods of machine learning is necessary. An empirical demonstration of the techniques for these methods is also provided on real data set benchmarks.

scholarly journals A Supervised Learning Algorithm to Forecast Weather Conditions for Playing Cricket

2019 ◽  
Vol 9 (1) ◽  
pp. 1560-1565
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cloud Computing ◽  
Big Data ◽  
Internet Of Things ◽  
Supervised Learning ◽  
Learning Algorithm ◽  
Weather Conditions ◽  
Redundant Data ◽  
Classification Technique

Now days, Machine learning is considered as the key technique in the field of technologies, such as, Internet of things (IOT), Cloud computing, Big data and Artificial Intelligence etc. As technology enhances, lots of incorrect and redundant data are collected from these fields. To make use of these data for a meaningful purpose, we have to apply mining or classification technique in the real world. In this paper, we have proposed two nobel approaches towards data classification by using supervised learning algorithm

scholarly journals Predictive Analysis of Taxi Fare using Machine Learning

2020 ◽  
pp. 373-378
Author(s):  
Pallab Banerjee ◽  
Biresh Kumar ◽  
Amarnath Singh ◽  
Priyeta Ranjan ◽  
Kunal Soni
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Comparative Analysis ◽  
Supervised Learning ◽  
Analysis Of Algorithms ◽  
Learning Technologies ◽  
Predictive Analysis ◽  
Prediction Problem ◽  
Prediction Modeling

This research aims to study the predictive analysis, which is a method of analysis in Machine Learning. Many companies like Ola, Uber etc uses Artificial Intelligence and machine learning technologies to find the solution of accurate fare prediction problem. We are proposing this paper after comparative analysis of algorithms like regression and classification, which are useful for prediction modeling to get the most accurate value. This research will be helpful to those, who are involved in fare forecasting. In previous era, the fare was only dependent on distance, but with the enhancement in technologies the cab’s fare is dependent on a lot of factors like time, location, number of passengers, traffic, number of hours, base fare etc. The study is based on Supervised learning whose one application is prediction, in machine learning.

Machine Learning and Data Mining in Bioinformatics

2012 ◽  
pp. 695-703
Author(s):  
George Tzanis ◽  
Christos Berberidis ◽  
Ioannis Vlahavas
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Mining ◽  
Reinforcement Learning ◽  
Supervised Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
The World ◽  
Supervised Learning Algorithms ◽  
Computational Systems

Machine learning is one of the oldest subfields of artificial intelligence and is concerned with the design and development of computational systems that can adapt themselves and learn. The most common machine learning algorithms can be either supervised or unsupervised. Supervised learning algorithms generate a function that maps inputs to desired outputs, based on a set of examples with known output (labeled examples). Unsupervised learning algorithms find patterns and relationships over a given set of inputs (unlabeled examples). Other categories of machine learning are semi-supervised learning, where an algorithm uses both labeled and unlabeled examples, and reinforcement learning, where an algorithm learns a policy of how to act given an observation of the world.

Machine Learning and Data Mining in Bioinformatics

2009 ◽  
pp. 612-621 ◽  
Author(s):  
George Tzanis ◽  
Christos Berberidis ◽  
Ioannis Vlahavas
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Mining ◽  
Reinforcement Learning ◽  
Supervised Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
The World ◽  
Supervised Learning Algorithms ◽  
Computational Systems

Machine learning is one of the oldest subfields of artificial intelligence and is concerned with the design and development of computational systems that can adapt themselves and learn. The most common machine learning algorithms can be either supervised or unsupervised. Supervised learning algorithms generate a function that maps inputs to desired outputs, based on a set of examples with known output (labeled examples). Unsupervised learning algorithms find patterns and relationships over a given set of inputs (unlabeled examples). Other categories of machine learning are semi-supervised learning, where an algorithm uses both labeled and unlabeled examples, and reinforcement learning, where an algorithm learns a policy of how to act given an observation of the world.

scholarly journals Pemanfaatan Machine Learning dalam Berbagai Bidang: Review paper

2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Ahmad Roihan ◽  
Po Abas Sunarya ◽  
Ageng Setiani Rafika
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Reinforcement Learning ◽  
Unsupervised Learning ◽  
Supervised Learning ◽  
Review Paper ◽  
Computational Costs ◽  
Accuracy And Precision ◽  
Learning Reinforcement ◽  
High Level

Abstrak - Pembelajaran mesin merupakan bagian dari kecerdasan buatan yang banyak digunakan untuk memecahkan berbagai masalah. Artikel ini menyajikan ulasan pemecahan masalah dari penelitian-penelitian terkini dengan mengklasifikasikan machine learning menjadi tiga kategori: pembelajaran terarah, pembelajaran tidak terarah, dan pembelajaran reinforcement. Hasil ulasan menunjukkan ketiga kategori masih berpeluang digunakan dalam beberapa kasus terkini dan dapat ditingkatkan untuk mengurangi beban komputasi dan mempercepat kinerja untuk mendapatkan tingkat akurasi dan presisi yang tinggi. Tujuan ulasan artikel ini diharapkan dapat menemukan celah dan dijadikan pedoman untuk penelitian pada masa yang akan datang.Katakunci: pembelajaran mesin, pembelajaran reinforcement, pembelajaran terarah, pembelajaran tidak terarahAbstract - Machine learning is part of artificial intelligence that is widely used to solve various problems. This article reviews problem solving from the latest studies by classifying machine learning into three categories: supervised learning, unsupervised learning, and reinforcement learning. The results of the review show that the three categories are still likely to be used in some of the latest cases and can be improved to reduce computational costs and accelerate performance to get a high level of accuracy and precision. The purpose of this article review is expected to be able to find a gap and it is used as a guideline for future research.Keywords: machine learning, reinforcement learning, supervised learning, unsupervised learning

scholarly journals MAKALAH MACHINE LEARNING

2021 ◽  
Author(s):  
Andreas M Simanullang
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Social Media ◽  
Unsupervised Learning ◽  
Supervised Learning

Machine learning merupakan sistem yang mampu belajar sendiri untuk memutuskan sesuatu tanpa harus berulangkali diprogram oleh manusia sehingga komputer menjadi semakin cerdas berlajar dari pengalaman data yang dimiliki. Berdasarkan teknik pembelajarannya, dapat dibedakan supervised learning menggunakan dataset (data training) yang sudah berlabel, sedangkan unsupervised learning menarik kesimpulan berdasarkan dataset. Input berupa dataset digunakan pembelajaran mesin untuk menghasilkan analisis yang benar. Penyelesaian digunakan Python yang menyediakan algoritma dan library yang digunakan membuat machine learning. Artificial intelligence (AI) dan machine learning saat ini kembali memasuki fase booming setelah beberapa dekade mengalami pasang surut. Kecerdasan Buatan kembali digandrungi, dimana penerapannya dilakukan secara masive pada aplikasi-aplikasi bisnis dan social media jaman now seperti Facebook, Twitter, Google, Amazon, dan bahkan berbagai aplikasi besar dari Indonesia seperti Go-jek, Tokopedia, dan sebagainya. Struktur pembahasan dalam buku ini meliputi 3 bagian besar yaitu (1) Konsep Machine Learning dan Kecerdasan Buatan (2) Dasar-Dasar Pemrograman Python untuk Machine Learning serta (3) Contoh Penerapan Machine Learning Menggunakan Python dengan menerapkan beberapa algoritma baik Supervised maupun Unsupervised Learning. Beberapa studi kasus dibahas secara lengkap dari pemahaman algoritma, mengolah dataset sampai pada training dan testing serta visualisasi hasil dari model machine learning yang dikembangkan.

scholarly journals A Genetic Algorithm for Finding a Small and Diverse Set of Recent News Stories on a Given Subject: How We Generate AAAI’s AI-Alert

2019 ◽  
Vol 33 ◽  
pp. 9357-9364
Author(s):  
Joshua Eckroth ◽  
Eric Schoen
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Genetic Algorithm ◽  
Supervised Learning ◽  
A Priori ◽  
News Stories ◽  
Scoring Algorithm ◽  
Representative Selection ◽  
Story Selection ◽  
Selection Of

This paper describes the genetic algorithm used to select news stories about artificial intelligence for AAAI’s weekly AIAlert, emailed to nearly 11,000 subscribers. Each week, about 1,500 news stories covering various aspects of artificial intelligence and machine learning are discovered by i2k Connect’s NewsFinder agent. Our challenge is to select just 10 stories from this collection that represent the important news about AI. Since stories and topics do not necessarily repeat in later weeks, we cannot use click tracking and supervised learning to predict which stories or topics are most preferred by readers. Instead, we must build a representative selection of stories a priori, using information about each story’s topics, content, publisher, date of publication, and other features. This paper describes a genetic algorithm that achieves this task. We demonstrate its effectiveness by comparing several engagement metrics from six months of “A/B testing” experiments that compare random story selection vs. a simple scoring algorithm vs. our new genetic algorithm.

Graph-Based Semi-Supervised Learning With Big Data

2017 ◽  
pp. 154-185
Author(s):  
Prithish Banerjee ◽  
Mark Vere Culp ◽  
Kenneth Jospeh Ryan ◽  
George Michailidis
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Supervised Learning ◽  
Prior Knowledge ◽  
Linear Algebra ◽  
Real Data ◽  
Data Set ◽  
Regression Problems ◽  
Classification And Regression ◽  
Empirical Demonstration

This chapter presents some popular graph-based semi-supervised approaches. These techniques apply to classification and regression problems and can be extended to big data problems using recently developed anchor graph enhancements. The background necessary for understanding this Chapter includes linear algebra and optimization. No prior knowledge in methods of machine learning is necessary. An empirical demonstration of the techniques for these methods is also provided on real data set benchmarks.

Classification Based on Supervised Learning

2011 ◽  
pp. 305-347
Author(s):  
Yu Wang
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Supervised Learning ◽  
Binary Classification ◽  
Learning Task ◽  
Training Sample ◽  
Machine Learning Algorithms ◽  
Network Attacks ◽  
Response Variable ◽  
Fundamental Information

Classification plays an important role in network security. It classifies network traffic into different categories based on the characteristics of the traffic and aims to prevent network attacks by detecting intrusion as early as possible. If a labeled response variable is available then the classification belongs to the statistically supervised learning theme. The term ”supervised learning” comes from the Artificial Intelligence field where research is focused on machine learning (Nilsson, 1996). In general, a supervised learning task can be described by giving a training sample with known patterns, f, represented by predictors, X, and a labeled response variable, Y , to select 1, 2 ( , , ) g y y y y Y = ? ? values for new 1 2 ( , , ) k x x x x X = ? ? values. 1 2 ( , , ) g Y y y y ? may be either a binary class, or multilevel classes, ( 2) g > . As we discussed previously, these classes cannot be determined absolutely and they are based on the degree of our belief, which is expressed in terms of probability (Woodworth, 2004). In this chapter, we will focus mainly on the binary classification task and we will discuss several modeling approaches, including both parametric and nonparametric methods. Readers who are interested in obtaining fundamental information on supervised learning and machine learning algorithms should refer to Lane & Brodley (1997), Vapnik (1998, 1999), Hosmer & Lemeshow (2000), Duda, Hart & Stork (2001), Hastie, Tibshirani & Friedman (2001), Müller Mika, Rätsch, Tsuda & Schölkopf (2001), Herbrich (2002), Vittinghoff, Glidden, Alpaydin (2004), Shiboski & McCulloch (2005), Maloof (2006), Neuhaus & Bunke (2007), and Diederich (2008).

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