Search Results for "support vector machine"

XiangShan is an open-source, high-performance RISC-V processor project that implements out-of-order superscalar cores using Chisel for hardware construction. The design targets modern performance goals—deep pipelines, speculative execution, multi-issue decode/execute, and sophisticated branch prediction—while remaining synthesizable for ASIC flows and portable to FPGAs for research. A modular microarchitecture separates frontend, backend, and memory subsystems with coherent caches and...

Apache Spark is a unified engine for large-scale data processing, offering APIs for batch jobs, streaming, machine learning, and graph computation. It builds on resilient distributed datasets (RDDs) and the newer DataFrame/Dataset abstractions to provide fault-tolerant, in-memory computation across clusters. Spark’s execution engine handles scheduling, shuffles, caching, and data locality so users can focus on transformations rather than infrastructure plumbing. With Spark Streaming...

The riscv-boom project (also called BOOM or SonicBOOM) implements a high-performance, synthesizable out-of-order RISC-V core written in the Chisel hardware construction language. It targets the RV64GC (i.e. 64-bit with general + compressed + floating point) instruction set and supports features such as virtual memory, caches, atomics, and IEEE-754 floating point. The design is parameterizable, meaning users can tune pipeline widths, buffer sizes, functional units, and other...

The node2vec project provides an implementation of the node2vec algorithm, a scalable feature learning method for networks. The algorithm is designed to learn continuous vector representations of nodes in a graph by simulating biased random walks and applying skip-gram models from natural language processing. These embeddings capture community structure as well as structural equivalence, enabling machine learning on graphs for tasks such as classification, clustering, and link prediction. The repository contains reference code accompanying the research paper node2vec: Scalable Feature Learning for Networks (KDD 2016). ...

Apache PredictionIO® is an open source Machine Learning Server built on top of a state-of-the-art open source stack for developers and data scientists to create predictive engines for any machine learning task. Quickly build and deploy an engine as a web service on production with customizable templates; respond to dynamic queries in real-time once deployed as a web service; evaluate and tune multiple engine variants systematically; unify data from multiple platforms in batch or in real-time for comprehensive predictive analytics; speed up machine learning modeling with systematic processes and pre-built evaluation measures; support machine learning and data processing libraries such as Spark MLLib and OpenNLP; implement your own machine learning models and seamlessly incorporate them into your engine; simplify data infrastructure management.

Math expression parser and evaluator written in Scala. Usable from Java (Sun JRE 1.6) Provides float, integral, boolean and vector data types, some string processing support. Variables may be defined internally or im- and exported through a binding.