In a more individual passion project, I crafted aKNN Forecast Machine Learning TradingViewIndicatorandAlgorithm. Here, I created a K-Nearest Neighbors regression model to forecast future price points, yielding a remarkable 2461.42% return on TSLA and 767.49% return on NVDA. Additionally, I enhanced ...
5. K-Nearest Neighbors (KNN) Imputation K-nearest neighbors (KNN) Imputation is a method for handling missing data by estimating missing values using the values of their K-nearest neighbors, which are determined based on a similarity metric (e.g., Euclidean distance or cosine similarity) in t...
The kNN algorithm can be used to address classification and regression problems. As an example, the classification of an unlabeled image can be determined by the labels assigned to its nearest neighbors. This is useful for recommendation systems, anomaly detection, and image/text classificat...
IN-CONTEXT REINFORCEMENT LEARNING WITH ALGORITHM DISTILLATION; Michael Laskin et al Supervised Pretraining Can Learn In-Context Reinforcement Learning; Jonathan N. Lee et al Learning to Retrieve In-Context Examples for Large Language Models; Liang Wang et al IN-CONTEXT LEARNING IN LARGE LANGUAGE MODEL...
2018.10 [checkpoint] Labeless Part 5: How to Decrypt Strings in Boleto Banking Malware Without Reconstructing Decryption Algorithm. - Check Point Research 2018.10 [checkpoint] Labeless Part 4: Scripting - Check Point Research 2018.08 [checkpoint] Labeless Part 3: How to Dump and Auto-Resolve WinAPI...
What is the maximum number of shards that can be allocated for indexes on a single data node in an Elasticsearch cluster? How are indexes whose names start with .monitoring-es generated? What can I do with such indexes? What encryption algorithm is used to encrypt disks for an Elasticsearch...
Model entities and relations in the embedding/vector spaceℝd. Given a true triple(ℎ, r ,t), the goal is that the embedding of(ℎ, r)should be close to the embedding oft. How to embed ℎ,r? How to define closeness? Answer in TransE algorithm. ...
[5], and Guo et al. also proposed anindividual calibration curve for each participant [6].In a recent publication from Vencat et al. [7], theauthorssuggested using ML to overcome those limitations onreflective pulse oximetry on the finger. Their modelimplementedk-Nearest Neighbor(kNN),...
Other details also have a huge impact on the data structure and performance of the energy demand model, including the choice of influential factors, the historical time window of factors selected, the split between training and test data, and the choice of machine learning (ML) algorithm. There...
where you sample random points to estimate the probability of an event too complicated to sample from directly. A machine learning viewpoint of the same problem is that in dimensiond, if your machine learning algorithm requires a representative sample of the input space in order to make a usefu...