Recently,　manifold　learning　and　semi-supervised　learning　are　two　hot　topics　in　the　field　of　machine　learning.　But　there　are　only　a　few　researches　on　semi-supervised　learning　from　the　point　of　manifold　learning,　especially　for　semi-supervised　regression.　In　this　paper,　semi-supervised　regression　on　manifolds　is　studied,　which　can　employ　the　manifold　structure　hidden　in　datasets　to　the　problem　of　regression　estimation.　Firstly　the　framework　of　Laplacian　regularization　presented　by　M.　Belkin　et　al.　is　introduced.　Then　the　framework　of　Laplacian　semi-supervised　regression　with　a　class　of　generalized　loss　functions　is　deduced.　Under　this　framework,　Laplacian　semi-supervised　regression　algorithms　with　linear　epsi;-insensitive　loss　functions,　quadric　epsi;-insensitive　loss　functions　and　Huber　loss　functions　are　presented.　Their　experimental　results　on　S-curve　dataset　and　Boston　Housing　dataset　are　given　and　analyzed.　The　problem　of　semi-supervised　regression　on　manifolds　is　interesting　but　quite　difficult.　The　aim　of　this　paper　is　only　to　accumulate　some　experience　for　further　research　in　the　future.　There　are　still　many　hard　problems　on　semi-supervised　regression　estimation　on　manifolds,　such　as　constructing　statistical　basis　of　the　algorithm,　looking　for　better　graph　regularizer　in　the　framework　of　Laplacian　semi-supervised　regression,　designing　quicker　algorithms,　implementing　the　algorithm　on　more　datasets　and　so　on.