Sökning: "Apprendimento automatico"

Hittade 2 uppsatser innehållade orden Apprendimento automatico.

1. 1. Customer churn prediction in a slow fashion e-commerce context : An analysis of the effect of static data in customer churn prediction

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Luca Colasanti; [2023]
   Nyckelord :Survival Analysis; Time To Event prediction; Churn retention; Machine Learning; Deep Learning; Customer Clustering; E-commerce; Analisi di sopravvivenza; Previsione del tempo a evento; Ritenzione dall’abbandono dei clienti; Apprendimento automatico; Apprendimento profondo; Segmentazione della clientela; Commercio elettronico; Överlevnadsanalys; Tid till händelseförutsägelse; Churn Prediction; Maskininlärning; Djuplärning; Kundkluster; E-handel;

   Sammanfattning : Survival analysis is a subfield of statistics where the goal is to analyse and model the data where the outcome is the time until the occurrence of an event of interest. Because of the intrinsic temporal nature of the analysis, the employment of more recently developed sequential models (Recurrent Neural Network (RNN) and Long Short Term Memory (LSTM)) has been paired with the use of dynamic temporal features, in contrast with the past reliance on static ones. LÄS MER

3. 2. Dynamic Graph Embedding on Event Streams with Apache Flink

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Massimo Perini; [2019]
   Nyckelord :Dynamic Graph; Representation Learning; Stream; Real-Time Data Processing; Scalable Graph Processing; Graph Neural Network; Experience Replay; Grafi dinamici; Representation Learning; Flussi di dati; Elaborazione in tempo reale; Elaborazione di grafi scalabile; Reti neurali per grafi; Experience Replay; Dynamisk graf; Representationsinlärning; ström; databehandling i realtid; skalbar grafbehandling; grafiskt neuralt nätverk; erfarenhetsåterspelning;

   Sammanfattning : Graphs are often considered an excellent way of modeling complex real-world problems since they allow to capture relationships between items. Because of their ubiquity, graph embedding techniques have occupied research groups, seeking how vertices can be encoded into a low-dimensional latent space, useful to then perform machine learning. LÄS MER