Podcasts about naive bayes

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.07.08.548198v1?rss=1 Authors: Anderson, C. J., Cadeddu, R., Anderson, D. N., Huxford, J. A., VanLuik, E. R., Odeh, K., Pittenger, C., Pulst, S. M., Bortolato, M. Abstract: Background. Self-grooming behavior in rodents serves as a valuable model for investigating stereotyped and perseverative responses. Most current grooming analyses primarily rely on video observation, which lacks standardization, efficiency, and quantitative information about force. To address these limitations, we developed an automated paradigm to analyze grooming using a force-plate actometer. New Method. Grooming behavior is quantified by calculating ratios of relevant movement power spectral bands. These ratios are then input into a naive Bayes classifier, trained with manual video observations. To validate the effectiveness of this method, we applied it to the behavioral analysis of the early-life striatal cholinergic interneuron depletion (CIN-d) mouse, a model of tic pathophysiology recently developed in our laboratory, which exhibits prolonged grooming responses to acute stressors. Behavioral monitoring was simultaneously conducted on the force-place actometer and by video recording. Results. The naive Bayes approach achieved 93.7% accurate classification and an area under the receiver operating characteristic curve of 0.894. We confirmed that male CIN-d mice displayed significantly longer grooming durations compared to controls. However, this elevation was not correlated with increases in grooming force. Notably, haloperidol, a benchmark therapy for tic disorders, reduced both grooming force and duration. Comparison with Existing Methods. In contrast to observation-based approaches, our method affords rapid, unbiased, and automated assessment of grooming duration, frequency, and force. Conclusions. Our novel approach enables fast and accurate automated detection of grooming behaviors. This method holds promise for high-throughput assessments of grooming stereotypies in animal models of tic disorders and other psychiatric conditions. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Probabilities play a big part in AI and machine learning. After all, AI systems are Probabilistic systems that must learn what to do. In this episode of the AI Today podcast hosts Kathleen Walch and Ron Schmelzer define the terms Bayes' Theorem, Bayesian Classifier, Naive Bayes, and explain how they relate to AI and why it's important to know about them. Continue reading AI Today Podcast: AI Glossary Series – Bayes' Theorem, Bayesian Classifier, Naive Bayes at AI & Data Today.

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.08.24.505211v1?rss=1 Authors: Ozcan, F., Alkan, A. Abstract: Natural sounds are easily perceived and identified by humans and animals. Despite this, the neural transformations that enable sound perception remain largely unknown. Neuroscientists are drawing important conclusions about neural decoding that may eventually aid research into the design of brain-machine interfaces (BCIs). It is thought that the time-frequency correlation characteristics of sounds may be reflected in auditory assembly responses in the midbrain and that this may play an important role in identification of natural sounds. In our study, natural sounds will be predicted from multi-unit activity (MUA) signals collected in the inferior colliculus. The temporal correlation values of the MUA signals are converted into images. We used two different segment sizes and thus generated four subsets for the classification. Using pre-trained convolutional neural networks (CNNs), features of the images were extracted and the type of sound heard was classified. For this, we applied transfer learning from Alexnet, GoogleNet and Squeezenet CNNs. The classifiers support vector machines (SVM), k-nearest neighbour (KNN), Naive Bayes and Ensemble were used. The accuracy, sensitivity, specificity, precision and F1 score were measured as evaluation parameters. Considering the trials one by one in each, we obtained an accuracy of 85.69% with temporal correlation images over 1000 ms windows. Using all trials and removing noise, the accuracy increased to 100%. Copy rights belong to original authors. Visit the link for more info Podcast created by PaperPlayer

05 - GDA & Naive Bayes

Links from the show:GitHub - Assisted-Mindfulness/naive-bayes: Naive Bayes works by looking at a training set and making a guess based on that set.Google AI researcher explains why the technology may be 'sentient' : NPRFirefox Rolls Out Total Cookie Protection By Default To All Usershttps://apnews.com/article/internet-explorer-shutting-down-e45abf1df9d34c135e41a01cf7d96c25https://twitter.com/nauleyco/status/1537430500523905024PhpStorm 2022.2 Early Access Program Is Open | The PhpStorm BlogElon Musk sued for $258 billion over alleged Dogecoin pyramid scheme | ReutersSymfony 6.1.0 released (Symfony Blog)GitHub - php-fig/per-coding-style: PER coding stylePHP: rfc:auto-capture-closureThis episode of PHPUgly was sponsored by:Honeybadger.io - https://www.honeybadger.io/PHPUgly streams the recording of this podcast live. Typically every Thursday night around 9 PM PT. Come and join us, and subscribe to our Youtube Channel, Twitch, or Periscope. Also, be sure to check out our Patreon Page.Twitter Account https://twitter.com/phpuglyHost:Eric Van JohnsonJohn CongdonTom RideoutStreams:Youtube ChannelTwitchPowered by RestreamPatreon PagePHPUgly Anthem by Harry Mack / Harry Mack Youtube ChannelThanks to all of our Patreon Sponsors:Honeybadger ** This weeks Sponsor **ButteryCrumpetFrank WDavid QShawnKen FBoštjanMarcusShelby CS FergusonRodrigo CBillyDarryl HKnut Erik BDmitri GElgimboMikePageDevKenrick BKalen JR. C. S.Peter AClayton SRonny MBen RAlex BKevin YEnno RWayneJeroen FAndy HSeviChris CSteve MRobert SThorstenEmily JJoe FAndrew WulrikJohn CJames HEric MLaravel MagazineEd GRirielilHermitChamp

MultinomalNB

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.10.26.354357v1?rss=1 Authors: Ramos, T., Galindo, N., Arias-Carrasco, R., da Silva, C., Maracaja-Coutinho, V., do Rego, T. Abstract: Non-coding RNAs (ncRNAs) are important players in the cellular regulation of organisms from different kingdoms. One of the key steps in ncRNAs research is the ability to distinguish coding/non-coding sequences. We applied 7 machine learning algorithms (Naive Bayes, SVM, KNN, Random Forest, XGBoost, ANN and DL) through 15 model organisms from different evolutionary branches. Then, we created a stand-alone and web server tool (RNAmining) to distinguish coding and non-coding sequences, selecting the algorithm with the best performance (XGBoost). Firstly, we used coding/non-coding sequences downloaded from Ensembl (April 14th, 2020). Then, coding/non-coding sequences were balanced, had their tri-nucleotides counts analysed and we performed a normalization by the sequence length. Thus, in total we built 180 models. All the machine learning algorithms tests were performed using 10-folds cross-validation and we selected the algorithm with the best results (XGBoost) to implement at RNAmining. Best F1-scores ranged from 97.56% to 99.57% depending on the organism. Moreover, we produced a benchmarking with other tools already in literature (CPAT, CPC2, RNAcon and Transdecoder) and our results outperformed them, opening opportunities for the development of RNAmining, which is freely available at https://rnamining.integrativebioinformatics.me/ . Copy rights belong to original authors. Visit the link for more info

The results from machine learning have been getting better and better and the results seen so far from OpenAI's GPT-3 model look stunningly good. But unlike GPT-2 (which was publicly released under a free license), so far GPT-3 is accessible via API-only. What's the reasoning and possible impact of that decision? For that matter, what kind of impacts could machine learning advancements make on FOSS, programming in general, art production, and civic society?Links:The OpenAI's GPT-3 may be the biggest thing since bitcoin articleThe quoted comment on Hacker NewsAuto-generation of legalese and auto-web-design GPT-3 demosGPT-2GPT-3's API and FAQ pageTensorflow and PyTorch(Artificial) neural networks and machine learninghttps://thispersondoesnotexist.com/Google's Deepmind and Agent57 (be sure to watch the Agent57 videos, they're's impressive)Mozilla's Common Voice projectAlphaGoBayesian spam filters; see also Paul Graham's highly influential a plan for spam writeupMarkov chains (we miss you, X11R5...)The Postmodernist Essay GeneratorPostmodernismNeural networks' difficulties in explaining "why they did that" and an overview of attempts to make things better: An Overview of Interpretability of Machine LearningChris had a conversation with Gerald Sussman about AI that was related to the above and influential on them. "If an AI driven car drives off the side of the road, I want to know why it did that. I could take the software developer to court, but I would much rather take the AI to court."The Propagator Model (by Alexey Radul and Gerald Jay Sussman, largely): Revised Report on the Propagator Model. See also: We Really Don't Know How to Compute!Three Panel Soul's Recursion comic (cut from this episode, but we also originally mentioned their Techics comic which is definitely relevant though)Surrealism, Abstract Expressionism, Impressionism, and the Realism movement (Obviously there's also a lot more to say about these art movements than just lumping them as a reaction to photography but... only so much time on an episode.)AI Dungeon 2 (nonfree, though you can play it in your browser)Episode of Ludology about procedural narrative generationImplicit Bias and the Teaching of WritingMachine learning's tendency to inherit biasesRise of the racist robots -- how AI is learning all our worst impulsesMachine bias (and its use in deciding court cases)Google's Vision AI producing racist resultsWhen It Comes to Gorillas, Google Photos Remains Blind (Content warning: this is due to an extremely harmful form of synthesized racism from the biases in the datasets Google has used)Predictive policing algorithms are racist. They need to be dismantled.Discovering Reinforcement Learning Algorithms and the subdiscipline of Learning to LearnHayo Miazaki's criticism of an AI demonstration not considering its impact

In this podcast Mark Bell (TNA) and Leontien Talboom (UCL and TNA) describe the machine learning club they have set up at the UK National Archives (TNA) to help archivists at TNA develop their AI literacy.   They describe how they have taken the group through a series of stages to introduce them to data science, to give them experience of preparing data, and to enable them to develop their intuitions about how different machine learning models work (Naive Bayes, Support vector machines etc.), and an understanding of the challenges that AI can (and can't) be used to tackle.   They also describe how they intend to move on to discuss some key issues in the application of AI to records, including the issue of the explainability of AI decisions and the issue of capturing the machine learning model itself as a record.   Mark Bell is Senior digital researcher at TNA.  Leontien Talboom  is a doctoral researcher working with UCL's Department of Information Studies and with TNA.   Mark and Leontien were interviewed by James Lappin on 22 May 2020.   Links: Mark Bell profile:  https://www.nationalarchives.gov.uk/about/our-research-and-academic-collaboration/our-research-and-people/staff-profiles/mark-bell/   Leontein Talboom profile: https://www.ucl.ac.uk/information-studies/leontien-talboom   James Lappin blog https://thinkingrecords.co.uk/

Neste episódio compartilho o que aprendi lendo a documentação do Scikit-learn sobre Gaussian Processes, Naive Bayes e uma reflexão sobre como ensinamos machine learning. Se você quiser aprender a habilidade mais importante para trabalhar com Data Science, acesse http://CursoDeDataScience.com Juntei todas as minhas dicas de machine learning num e-book. Conheça http://www.ManualDeDataScience.com Me siga no Instagram e receba dicas exclusivas: http://www.instagram.com/mariofilhoml

Organisations which sell services or products to mass audiences struggle to understand the reasons why their customers are happy, and especially how the preferences evolve over time. The problem of understanding reasons for customer satisfaction over time stems from methodological difficulty in analysing written customer reviews as time series data. This project trials use of sentiment analysis to understand evolution of feedback over time. Sentiment models trained using Recurrent Neural Network, Naive Bayes and Maximum Entropy are compared and the best model is selected to predict feedback in the future. Difference in predictive accuracy over time is assessed for the selected model. Moreover, visuals are developed to depict how text features and themes vary in importance when it comes to accurate prediction of satisfaction over time. The objective is to enable real-time visualization and understanding of patterns in customer feedback over time from big text corpora --- Support this podcast: https://anchor.fm/fim/support

In this episode of the SuperDataScience Podcast, I chat with data scientist Ayodele Odubela. You will hear how and why she chose to do a Masters in Data Science and supplemented that with online education. You will also hear about self-discovery, fortitude and passion, and how she got one of her data science jobs through Twitter. You will learn about some of Ayodele's projects like using SVM for detecting poisonous vs. edible mushrooms, using random forests and decision trees for ranking wines based on the chemical contents, using the Naive Bayes to detect spam. You will learn about the real-world project that she's worked on, bullet stopping flying drones. You will find out what role machine learning played in that project and how they're going to be applied in society once they get rolled out. If you enjoyed this episode, check out show notes, resources, and more at www.superdatascience.com/297

Wir wollten eigentlich eine weitere Einsteigerepisode aufnehmen und waren daher auf der Suche nach einem einfachen Beispiel für die Verwendung der grundlegenden Datenstrukturen. Leider ist das Beispiel doch nicht so supereinfach geworden und so ist das hier dann eher eine Episode zum Thema Naive Bayes. Hmm, auch nicht so uninteressant, wie ich finde :). Shownotes Unsere E-Mail für Fragen, Anregungen & Kommentare: hallo@python-podcast.de News aus der Szene FrOSCon - Deep Learning Workshop Generatoren Coroutinen SHORTHANDED NEWS Django Chat, Episode 23: Async Django - Andrew Godwin Django 3 - Async Roadmap Naive Bayes Naive Bayes (wikipedia) Naive Bayes jupyter notebook Defaultdict Support Vector Machine Word Embeddings SpaCy Techtiefen: SpaCy Techtiefen: Moderne Sprachverarbeitung BERT RoBERTa XLNet gpt-2 AlterEgo Picks isort pptop Öffentliches Tag auf konektom

Ever wonder how to automatically detect language from a script? How does Google do it? Ever wonder how Amazon knows whether you are searching for a product or a SKU on its search bar? We look into character-based text classifiers in this episode. We cover 2 types of models. First is the bag-of-words models such as Naive Bayes, logistic regression and vanilla neural network. Second we cover sequence models such as LSTMs and how to prepare your characters for the LSTMs including things like one-hot encoding, padding, creating character embeddings and then feeding these into LSTMs. We also cover how to set up and compile these sequence models. Thanks for listening, and if you find this content useful, please leave a review and consider supporting this podcast from the link below. --- Send in a voice message: https://anchor.fm/the-data-life-podcast/message Support this podcast: https://anchor.fm/the-data-life-podcast/support

The earliest efforts to apply machine learning to natural language tended to convert every token (every word, more or less) into a unique feature. While techniques like stemming may have cut the number of unique tokens down, researchers always had to face a problem that was highly dimensional. Naive Bayes algorithm was celebrated in NLP applications because of its ability to efficiently process highly dimensional data. Of course, other algorithms were applied to natural language tasks as well. While different algorithms had different strengths and weaknesses to different NLP problems, an early paper titled Scaling to Very Very Large Corpora for Natural Language Disambiguation popularized one somewhat surprising idea. For many NLP tasks, simply providing a large corpus of examples not only improved accuracy, but it also showed that asymptotically, some algorithms yielded more improvement from working on very, very large corpora. Although not explicitly in about NLP, the noteworthy paper The Unreasonable Effectiveness of Data emphasizes this point further while paying homage to the classic treatise The Unreasonable Effectiveness of Mathematics in the Natural Sciences. In this episode, Kyle shares a few thoughts along these lines with Linh Da. The discussion winds up with a brief introduction to Zipf's law. When applied to natural language, Zipf's law states that the frequency of any given word in a corpus (regardless of language) will be proportional to its rank in the frequency table.

Overview of Naive Bayes Algorithm. Sorry the audio quality is off. Ran out of time to redo. I also post daily videos and blog posts. I'll do better tomorrow. Thanks for listening!

Today's spam filters are advanced data driven tools. They rely on a variety of techniques to effectively and often seamlessly filter out junk email from good email. Whitelists, blacklists, traffic analysis, network analysis, and a variety of other tools are probably employed by most major players in this area. Naturally content analysis can be an especially powerful tool for detecting spam. Given the binary nature of the problem ( or ) its clear that this is a great problem to use machine learning to solve. In order to apply machine learning, you first need a labelled training set. Thankfully, many standard corpora of labelled spam data are readily available. Further, if you're working for a company with a spam filtering problem, often asking users to self-moderate or flag things as spam can be an effective way to generate a large amount of labels for "free". With a labeled dataset in hand, a data scientist working on spam filtering must next do feature engineering. This should be done with consideration of the algorithm that will be used. The Naive Bayesian Classifer has been a popular choice for detecting spam because it tends to perform pretty well on high dimensional data, unlike a lot of other ML algorithms. It also is very efficient to compute, making it possible to train a per-user Classifier if one wished to. While we might do some basic NLP tricks, for the most part, we can turn each word in a document (or perhaps each bigram or n-gram in a document) into a feature. The Naive part of the Naive Bayesian Classifier stems from the naive assumption that all features in one's analysis are considered to be independent. If  and  are known to be independent, then . In other words, you just multiply the probabilities together. Shh, don't tell anyone, but this assumption is actually wrong! Certainly, if a document contains the word algorithm, it's more likely to contain the word probability than some randomly selected document. Thus, , violating the assumption. Despite this "flaw", the Naive Bayesian Classifier works remarkably will on many problems. If one employs the common approach of converting a document into bigrams (pairs of words instead of single words), then you can capture a good deal of this correlation indirectly. In the final leg of the discussion, we explore the question of whether or not a Naive Bayesian Classifier would be a good choice for detecting fake news.    

2.22 analyzes Jimmy G’s choice of lady friends (0:40), scrutinizes the sports eclipse (2:24), presents a new series: the algorithmic challenge (4:32), provides a primer on TGFBI analysis (13:35), checks in with the craft beer scene (20:19), critiques a recent WaPo article on accents (21:12), glances at the new job opening with the Brew Crew (23:19), and ends with a review of Gattaca (24:10).

Book 3, Part N, Chapter 177: Conditional Independence, and Naive Bayes "Rationality: From AI to Zombies" by Eliezer Yudkowsky Independent audio book project by Walter and James http://from-ai-to-zombies.eu Original source entry: http://lesswrong.com/lw/o8/conditional_independence_and_naive_bayes/ The complete book is available at MIRI for pay-what-you-want: https://intelligence.org/rationality-ai-zombies/ Source and podcast licensed CC-BY-NC-SA, full text here: https://creativecommons.org/licenses/by-nc-sa/3.0/ Intro/Outro Music by Kevin MacLeod of www.incompetech.com, licensed CC-BY: http://incompetech.com/music/royalty-free/index.html?isrc=USUAN1100708

This episode is inspired by one of our projects for Intro to Machine Learning: given a writing sample, can you use machine learning to identify who wrote it? Turns out that the answer is yes, a person’s writing style is as distinctive as their vocal inflection or their gait when they walk. By tracing the vocabulary used in a given piece, and comparing the word choices to the word choices in writing samples where we know the author, it can be surprisingly clear who is the more likely author of a given piece of text. We’ll use a seminal paper from the 1960’s as our example here, where the Naive Bayes algorithm was used to determine whether Alexander Hamilton or James Madison was the more likely author of a number of anonymous Federalist Papers.

Book III: The Machine in the Ghost - Part N: A Human's Guide to Words - Conditional Independence and Naive Bayes

One of Android's main defense mechanisms against malicious apps is a risk communication mechanism which, before a user installs an app, warns the user about the permissions the app requires, trusting that the user will make the right decision. This approach has been shown to be ineffective as it presents the risk information of each app in a "stand-alone" fashion and in a way that requires too much technical knowledge and time to distill useful information.We introduce the notion of risk scoring and risk ranking for Android apps, to improve risk communication for Android apps, and identify three desiderata for an effective risk scoring scheme. We propose to use probabilistic generative models for risk scoring schemes, and identify several such models, ranging from the simple Naive Bayes, to advanced hierarchical mixture models. Experimental results conducted using real-world datasets show that probabilistic generative models significantly outperform existing approaches, and that Naive Bayes models give a promising risk scoring approach. About the speaker: Christopher Gates is a PhD student in the Computer Science department of Purdue University and a member of CERIAS. He received his Masters Degree in Computer Science in 2005 from Rutgers University, and then worked at a startup company in NYC before deciding to pursue his PhD. His research interests are in information security and machine learning. In particular, his research focuses on using data to help users make more informed and safer security decisions. His research advisor is Prof. Ninghui Li.

One of Android's main defense mechanisms against malicious apps is a risk communication mechanism which, before a user installs an app, warns the user about the permissions the app requires, trusting that the user will make the right decision. This approach has been shown to be ineffective as it presents the risk information of each app in a “stand-alone” fashion and in a way that requires too much technical knowledge and time to distill useful information. We introduce the notion of risk scoring and risk ranking for Android apps, to improve risk communication for Android apps, and identify three desiderata for an effective risk scoring scheme. We propose to use probabilistic generative models for risk scoring schemes, and identify several such models, ranging from the simple Naive Bayes, to advanced hierarchical mixture models. Experimental results conducted using real-world datasets show that probabilistic generative models significantly outperform existing approaches, and that Naive Bayes models give a promising risk scoring approach.