From 27974d3b5851697a6922b1414fbbb440dae93245 Mon Sep 17 00:00:00 2001 From: Yusuke Yamada Date: Tue, 8 May 2018 06:16:30 +0900 Subject: [PATCH 1/2] Remove unnecessary char --- ROADMAP.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ROADMAP.md b/ROADMAP.md index 0b53674275..8522d10760 100644 --- a/ROADMAP.md +++ b/ROADMAP.md @@ -56,7 +56,7 @@ In the meanwhile, we are looking for contributions. An easy place to start is t * Hybrid training of pipelines containing both DNN and non-DNN predictors * Additional ML tasks (*) * _Recommendation_ - Is a problem that can be phrased a: "For a given user, predict the ratings this user would give to the items that they have not explicitly rated yet" - * _Anomaly Detection_, also known as _outlier detection_. It is a task to identify items, events or observations which do not conform to an expected pattern in the dataset. Typical examples are: detecting credit card fraud, medical problems or errors in text. Anomalies are also referred to as outliers,  novelties, noise, deviations and exceptions + * _Anomaly Detection_, also known as _outlier detection_. It is a task to identify items, events or observations which do not conform to an expected pattern in the dataset. Typical examples are: detecting credit card fraud, medical problems or errors in text. Anomalies are also referred to as outliers, novelties, noise, deviations and exceptions * _Sequence Classification_ - learns from a series of examples in a sequence, and each item is assigned a distinct label, akin to a multiclass classification task * Additional Data source support * Data from SQL Databases, such as SQL Server From 4030bb557dbd9870a2d1ffe191066048a4f5734b Mon Sep 17 00:00:00 2001 From: Yusuke Yamada Date: Tue, 8 May 2018 06:17:33 +0900 Subject: [PATCH 2/2] Fixed typo --- ROADMAP.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ROADMAP.md b/ROADMAP.md index 8522d10760..bc08ad3a86 100644 --- a/ROADMAP.md +++ b/ROADMAP.md @@ -46,7 +46,7 @@ In the meanwhile, we are looking for contributions. An easy place to start is t * Generative Additive Models * [SymSGD](https://arxiv.org/pdf/1705.08030.pdf) -a fast linear SGD learner * Factorization Machines - * [ProtoNN and Bonsaii](https://www.microsoft.com/en-us/research/project/resource-efficient-ml-for-the-edge-and-endpoint-iot-devices/) for compact and effecient models + * [ProtoNN and Bonsaii](https://www.microsoft.com/en-us/research/project/resource-efficient-ml-for-the-edge-and-endpoint-iot-devices/) for compact and efficient models * Integration with other ML packages * Accord.NET * etc.